Experimental evaluation of comfort and safety inlight-duty vehicles

Tiago Manuel Gois Ferreira Gaspar Neves

Thesis to obtain the Master of Science Degree in

Mechanical Engineering

Supervisors: Prof. GoncaIo Nuno Antunes GoncaIvesProf. Joao ManueI Pereira Dias

Examination Committee

Chairperson: Prof. Mario ManueI GoncaIves da CostaSupervisor: Prof. GoncaIo Nuno Antunes GoncaIves

Member of the Committee: Prof. Luıs AIberto GoncaIves de Sousa

November 2014

Acknowledgments

First and foremost, I would like to thank my supervisors Prof. Goncalo Nuno Antunes Goncalves andProf. Joao Manuel Pereira Dias, for their valuable input and feedback throughout the development ofthis thesis and for the availability to share their knowledge, without which a successful end would not bepossible.

A special thanks to Joao Freire and Nuno Duarte for their useful contribute.

To my family for their support, help and incentive during all these years and for always believing in mycapabilities. A special thanks to my father.

To the Mecanicos for their friendship and for being my comrades in this journey.

To Marta for the moral support, incentive to keep moving forward when facing hard challenges andespecially for the patience shown all these years.

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Resumo

O conforto e seguranca representam dois dos parametros mais relevantes para o bem-estar dos pas-sageiros de qualquer tipo de veıculo, que sao habitualmente avaliados de forma subjectiva em funcaodas condicoes da via ou tipo de veıculo mas cuja forma mais objectiva sera avaliar as aceleracoes aque um passageiro esta sujeito. Com o advento de tecnologias de baixo custo para monitorizacao abordo da dinamica de um veıculo e possıvel classificar estes parametros sem intervencao do condutor.Acresce ainda o elevado interesse de seguradoras em poder tracar um perfil do condutor atraves dadeteccao de possıveis situacoes de risco.

Neste trabalho, foram registados dados de aceleracao, velocidade e coordenadas geograficas duranteensaios experimentais num ambiente de conducao real. Os dados de aceleracao foram posteriormentetratados e usados para a implementacao de metodos ja utilizados previamente por diferentes autores,sendo usados como referencia para validacao dos mesmos eventos que sao habitualmente percep-cionados como desconfortaveis ou inseguros.

Procurou-se ainda implementar metodos alternativos para avaliacao da seguranca e calculo da veloci-dade e distancia percorrida, de modo a nao so verificar a possibilidade de reduzir a dependencia doacelerometro no caso de falha do equipamento como tambem se tentou inserir pequenas correccoesna informacao da velocidade obtida a partir da porta OBD-II do veıculo.

Os resultados obtidos com os metodos utilizados apresentam uma boa correlacao com a percepcao dosocupantes do veıculo durante os ensaios, restando apenas algumas reservas quanto a classificacao deconforto e seguranca de eventos induzidos por elevadas aceleracoes laterais.

Palavras-chave: Aceleracao; Conforto; Seguranca; Dinamica; Monitorizacao a bordo; Usage-Based Insurance

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Abstract

Comfort and safety are two of the most relevant parameters for the well-being of the passengers of anykind of vehicle, usually evaluated in a subjective manner as a function of road conditions or the typeof vehicle but a more objective manner is evaluating the accelerations to which a passenger is subject.The advent of low-cost technology for on-board monitoring of vehicle dynamics makes it possible toevaluate these parameters without any driver intervention. Added to this is the high interest of insurancecompanies to be able to profile a driver through the detection of possibly risky situations.

For this work, acceleration, speed and geographic coordinate data were collected during experimentaltrials in a real driving situation. The acceleration data were then processed and used for the imple-mentation of methods previously used by different authors, with events that are usually perceived asuncomfortable or unsafe being used as reference for validation of the methods.

Further on, alternative methods were implemented for safety evaluation and to calculate speed anddistance, in order to not only attest the possibility of reducing the dependence on the accelerometer incase of equipment failure but also to introduce slight corrections into the speed information collectedthrough the vehicle’s OBD-II port.

The results obtained with the applied methods present a good correlation with the vehicle occupants’perception throughout the trials, remaining only some reservations about the classification of comfortand safety attributed to events where the major influence is originated by high lateral acceleration.

Keywords: Acceleration; Comfort; Safety; Dynamics; On-board monitoring; Usage-Based Insur-ance

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Contents

1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.3 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 State of the art 11

3 Methodology 273.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.1.1 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.1.2 Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.2 General Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.1 Distance Between Coordinates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.2 Numerical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.2.3 Data Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4 Dynamics 374.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.3 Results Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5 Comfort 475.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495.3 Results Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

6 Safety 636.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646.3 Results Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 666.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

7 Conclusions and Future Work 737.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

References 76

Appendix 79

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List of Tables

1.1 Summary of mentioned UBI service providers . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1 Threshold values regarding comfort conditions . . . . . . . . . . . . . . . . . . . . . . . . 202.2 Threshold values regarding safety conditions . . . . . . . . . . . . . . . . . . . . . . . . . 202.3 Abbreviated Injury Scale levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.4 ISO 2631-1 comfort guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1 Differentiation and integration relations between distance, speed and acceleration . . . . 313.2 Qualitative comparison of the main features of Moving Average and Butterworth filters . . 34

4.1 Speeds at which the vehicles are tested to test the speedometer accuracy . . . . . . . . . 374.2 Resulting distances for the trip, using the three methods . . . . . . . . . . . . . . . . . . . 43

5.1 ISO 2631-1 comfort guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.2 Mean value of the RMS of each axis during the trip . . . . . . . . . . . . . . . . . . . . . . 505.3 Lower and upper limits from ISO 2631-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525.4 Comparison of the percentage of time spent in each comfort condition, using either the

lower or upper limits of magnitude of vibration total values . . . . . . . . . . . . . . . . . . 525.5 Guidelines for comfort levels on speed bumps using speed as a reference . . . . . . . . . 58

6.1 Reference safety levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646.2 Percentage of time in each level, using the different evaluation methods . . . . . . . . . . 67

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List of Figures

1.1 Factors ranked as the highest priority in the new vehicle purchase decision . . . . . . . . 21.2 Vehicle features ranked as most important in the new vehicle purchase . . . . . . . . . . . 21.3 Importance attributed to certain vehicle features amongst all drivers surveyed . . . . . . . 31.4 Scheme of the Progressive Corporation patented system . . . . . . . . . . . . . . . . . . 51.5 Market size - share of the telematics-enabled policies in Europe and the US . . . . . . . . 6

2.1 Conflict graph with definition of serious conflict . . . . . . . . . . . . . . . . . . . . . . . . 142.2 Continuum of traffic events from undisturbed passages to fatal accidents . . . . . . . . . . 142.3 Scores for the three observers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4 Effects of braking instruction and speed on the mean maximum deceleration as reached

during the control of braking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.5 Relation between speed and crash rate on urban 60 km/h and rural 100 km/h roads. The

figure refers to a self-report study, developed by stopping drivers considered as fast orslow according to the traffic speed distribution and asking about their crash history. . . . . 17

2.6 Relationship between average speed and crash frequency on four urban road types . . . 182.7 Illustration of measures of acceleration (g) and jerk (g/s) during a brake manoeuvre . . . 192.8 A conceptual description of the event data recorder . . . . . . . . . . . . . . . . . . . . . . 192.9 Basicentric axes of the human body - seated position . . . . . . . . . . . . . . . . . . . . 222.10 Health guidance caution zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.1 GPS receiver antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2 The inside of the OBU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.3 OBU installed in the vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.4 Map showing the route used during the tests . . . . . . . . . . . . . . . . . . . . . . . . . 303.5 Comparison of the results applying the Moving Average (top) and Butterworth (bottom)

filters to the longitudinal axis data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.1 Coordinates slightly deviated from their real position . . . . . . . . . . . . . . . . . . . . . 394.2 Different speeds, including the integration of acceleration in a sample of five minutes . . . 404.3 Different total distances, including the integration of acceleration in a sample of five minutes 404.4 Flowchart of the dynamic speed prediction model . . . . . . . . . . . . . . . . . . . . . . . 414.5 Speeds comparison including the dynamic prediction . . . . . . . . . . . . . . . . . . . . . 424.6 Total distances comparison including the dynamic prediction . . . . . . . . . . . . . . . . 434.7 First model of the dynamic speed prediction, without any applied constraints . . . . . . . 44

5.1 Filtered longitudinal acceleration, with indication on hard braking events . . . . . . . . . . 485.2 Filtered vertical acceleration, with indication on high speed crossing of speed bumps and

crossing of a cobblestone section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

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5.3 Longitudinal acceleration: filtered data (blue) and RMS value (black) . . . . . . . . . . . . 505.4 Resulting magnitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515.5 Resulting magnitude, with the upper limit reference line for each comfort level . . . . . . . 525.6 RMS of the longitudinal component in the cobblestone pavement . . . . . . . . . . . . . . 535.7 Magnitude compared to the magnitude without including the root mean square of the

longitudinal acceleration in the cobblestone pavement . . . . . . . . . . . . . . . . . . . . 545.8 Speed profile vs. magnitude, while crossing the cobblestone pavement section . . . . . . 555.9 Speed, longitudinal acceleration and vertical acceleration variation while approaching and

crossing a speed bump at approximately 30 km/h . . . . . . . . . . . . . . . . . . . . . . . 555.10 Magnitude and root mean square of the longitudinal and vertical accelerations, crossing

a speed bump at approximately 30 km/h . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565.11 Magnitude and root mean square of the vertical acceleration, crossing a speed bump at

approximately 50 km/h . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575.12 Magnitude obtained while performing a hard braking event . . . . . . . . . . . . . . . . . . 585.13 Comparison of the root mean square of the lateral acceleration and the magnitude while

performing a left-right turn sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595.14 Classification in terms of safety using lateral acceleration in the left-right turn sequence . 60

6.1 Longitudinal Butterworth filtered acceleration . . . . . . . . . . . . . . . . . . . . . . . . . 656.2 Lateral Butterworth filtered acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656.3 Comparison between the three different methods to obtain longitudinal acceleration, in a

set of 500 seconds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 666.4 Longitudinal and lateral acceleration components with the reference lines of each safety

level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676.5 Longitudinal acceleration components obtained from the OBD and GPS with the reference

lines of each safety level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676.6 Lateral acceleration (blue) and dynamic speed prediction (black) during left-right turn se-

quence. The reference lines (cyan) represent the lateral acceleration safety levels. . . . . 686.7 Lateral acceleration (blue) and dynamic speed prediction (black) during cross of a round-

about. The reference lines (cyan) represent the lateral acceleration safety levels. . . . . . 696.8 Longitudinal acceleration during the braking event . . . . . . . . . . . . . . . . . . . . . . 706.9 Longitudinal acceleration during the braking event, obtained from the GPS coordinates . . 706.10 Longitudinal acceleration during the braking event, obtained from the OBD data . . . . . . 71

7.1 Coefficients of friction for different roadway surfaces, comparing dry and wet condition . . 75

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Nomenclature

av Acceleration vibration total value

aw Weighted axial root mean square acceleration

ABS Anti-lock Braking System

AIS Abbreviated Injury Scale

ASI Acceleration Severity Index

EDR Event Data Recorder

GPS Global Positioning System

LDV Light-Duty Vehicles

MSDV Motion Sickness Dose Value

NCAP New Car Assessment Programme

OBD-II On-board Diagnostics II

OBU On Board Unit

OEM Original Equipment Manufacturer

PAYD Pay As You Drive

RMS Root Mean Square

SPS Standard Positioning Service

TA Time to accident

UBI Usage-Based Insurance

UNECE United Nations Economic Comission for Europe

UTACV Urban Tracked Air Cushion Vehicle

VTV Vibration Total Value

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Chapter 1

Introduction

1.1 Motivation

In today’s world, there are more than one thousand million cars in operation. Out of this amount ofcars, more than half is concentrated in the northern hemisphere, mostly in the developed countries.Looking more closely to data available for the United States, one can see that more than 239 millionvehicles were in operation in 2010, making the country’s vehicle-to-person ratio the highest in the world,at 1:1,3 [1]. On average a driver spent 76 minutes per day driving his private vehicle and each privatevehicle was driven slightly more than 10.000 miles per year [2]. These data clearly show how importantprivate vehicle transportation is nowadays in a developed country, and with it comes a concern withthe possibility of developing better products in terms of safety and comfort for drivers and passengers.This is clearly stated in Mohr et al [3], that specifically for the premium segment, automotive originalequipment manufacturers (OEMs) could differentiate themselves with the help of design elements, newfeatures in infotainment and innovations directed at safety and comfort.

From the driver point of view, the perception of both comfort and safety is very important. In Koppel etal [4], data from Spanish and Swedish consumers was analysed from the answers to a questionnaire.The study found that vehicle safety is a top priority for the customers while comfort is at an intermediateto high level of importance during the purchase process. In general, participants were more likely toselect both safety (e.g. Euro NCAP) related factors and safety related features as their highest priority inthe vehicle purchase process, thus showing that the perception of safety is a big concern for Europeanconsumers. The results of the study can be seen in Figures 1.1 and 1.2.

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Figure 1.1: Factors ranked as the highest priority in the new vehicle purchase decision [4]

Figure 1.2: Vehicle features ranked as most important in the new vehicle purchase [4]

Vrkljan and Anaby [5] tried to find out what are the most important vehicle features for Canadian cus-tomers, from a list that included: storage, mileage, safety, price, comfort, performance, design, andreliability. Similarly to Koppel et al [4], safety was found to be a major concern for car buyers. Themaximum value for importance was selected by more than 50% of the customers for three of the eightfeatures surveyed, specifically: safety, reliability and mileage. Figure 1.3 shows the level of importanceof each of the evaluated features.

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Figure 1.3: Importance attributed to certain vehicle features amongst all drivers surveyed [5]

The main findings of this work led to the conclusion that compared to all other features, except forreliability, safety had the highest importance rating, with reliability being given a similar rating. Basically,safety and reliability were found to be the most important features as opposed to the two least important,performance and design [5].

Contrary to the results of the previous studies, a study with Australian consumers interviewed bothpre- and post-purchase [6], came to the conclusion that nor safety nor comfort are among their mainpriorities. For this study a list of 20 factors was created, where four were safety related (ANCAP, USCR,pedestrian aggressivity and vehicle aggressivity) and comfort was also included. Price, fuel efficiencyand reliability were the three factors selected more times as ”high” in priority, immediately followed bythe ANCAP rating, which was selected by 30 percent of participants. Comfort and performance rankedlower and the other safety-related factors were ranked lowest in priority. The factors ranked in the sameorder in the post-purchase survey.

While both consumers and car manufacturers are interested in comfort and safety, their interest is morefocused on the practicality of the subject, which is more useful from a marketing point of view. While forsafety the NCAP (New Car Assessment Programme) establishes the criteria for the quantification of theconditions in case of an accident, the evaluation of comfort is highly subjective and depends on eachperson’s perception.

The idea of establishing an objective evaluation for both comfort and safety has been the subject ofvarious researches since the 1940s [7], with relative success but with results that are very different fromeach other, turning the establishment of threshold values that are in accordance with the perceptionof an average person as the biggest challenge in this area. Techniques used for this have alwaysrevolved around vehicle dynamics, in-car observations and behaviour questionnaires but none of thoserepresents an easily replicable method, that can be used in large numbers in normal day driving withsmall financial investment. That is where third parties such as insurance companies interested in findingan objective way to evaluate comfort and safety conditions come into play, especially trying to developnew ways to define the premium for a customer. The traditional approach to classify risk in automobileliability insurance is to assign an insured to a tariff class. The various tariff classes are based on manycriteria, including, for example, type of car, insured’s occupation, region (urban vs. rural), and crashhistory [8]. Of all these criteria, driving history is the hardest to evaluate and to verify, due to thepossibility of unreported occurrences. The use of data logging for assessment of the different dynamiccharacteristics of the vehicle can provide the data that is needed to develop a more accurate system,

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in which the driving behaviour is analysed to determine if a certain driver is more accident prone or,at least, takes bigger risks behind the wheel by recurrently going over the safety limits. This kind ofevaluation using telematics is already being used both in the US and in Europe, usually to develop PayAs You Drive (PAYD) systems.

The concept of Pay As You Drive, from here on called Usage-Based Insurance (UBI), differs from thetraditional insurance because of the amount and type of information taken into account. While traditionalinsurance only takes into account data like driver’s age, accident history, type of vehicle and the age ofthe vehicle, the UBI model can take into account not only these criteria but also intends to reflect currentpattern behaviours of a driver, thus allowing for a dynamically adjusted insurance premium.

The emergence of low cost and widely available computers and electronic devices, allowed the devel-opment of small size equipments, usually called Event Data Recorder (EDR) or On Board Unit (OBU).These equipments are built using basic electronic components such as accelerometers, GPRS and GPSmodules, among others and are easily installed even inside a small city car without affecting the nor-mal use of the vehicle, making the use of dynamic quantities like acceleration and speed much easierand more interesting for the development of new methods to assess comfort and safety conditions andraising interest for potential investment in a new business area.

Nowadays, most of the equipment in use works through a connection to the on-board diagnostic port(OBD-II), mandatory since 1996 in the United States and 2000 in the European Union. The most basicsystems are based on travelled distance read from the odometer or GPS, amount of time that the caris driven, and in some cases, information about speed and the time of the day during which the car isdriven. Trying to get a deeper knowledge about the driver, it is possible to add more details that canaffect the premium, such as hard braking, rapid acceleration or hard cornering.

In the US market, one of the first companies to offer a UBI product was GMAC Insurance in 2004, avail-able for customers subscribing to OnStar. This system connects to the OBD-II port, giving informationabout the total mileage driven that latter on is used to set how much the insured can save. Previously,Progressive Insurance had already filed for a patent registry in 1996, for a system with the capability tomonitor and record: miles driven, type of road, speed, safety equipment used, time of the day driven,rate of acceleration, rate of braking and observation of traffic signs [9]. These features were clearlyahead of its time, as will be seen ahead in this chapter, as only recently most of them started to be used.Figure 1.4 shows a scheme of how the system works.

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Figure 1.4: Scheme of the Progressive Corporation patented system [9]

In spite of all the features of this system, the Snapshot insurance program created by ProgressiveCorporation only transmits information about the amount of hard brakes per 100 miles, time of day,specifically how often the customer drives between midnight and 4 AM and miles driven and taking nolocation information through the GPS signal.

In 2012, All State launched the Drivewise program that intends to reward safe driving. This systemrecords mileage, braking, speed and time of day when a customer is driving, by connecting to the OBD-II port. Apart from allowing the customer to get a lower premium, the recorded information can beaccessed by the customer in a website, to get some feedback on his behaviour and possibly adapt theway he drives.

In the Portuguese market, the OK GPS service is available. The service provides the possibility tobenefit of a discount in the premium as long as the insured fulfils some requisites. The requisites arerelated to the data acquired by the company in partnership with the Italian company Octo Telematicsincluding distance driven, speed, hours of utilization and location where the car was driven. The servicealso includes the possibility of sending the GPS signal to the assistance service, in case of theft if theinsured signals the situation and in case of accident if the system detects an impact with an accelerationover 2,5g.

A look at Bruneteau et al [10] shows that more insurance providers all over the world have been lookingat this new business area, confirming that there is a tremendous potential for investment. The studycounts 130 UBI trials and launches, with about 5 million UBI policies worldwide. According to the study,2013 was expected to be the year of maturity for the UBI technology.

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In terms of generated value, according to a report by SAS [11] Progressive reported over $1 billion inpremium revenue for UBI policies. The same report shows a chart (Figure 1.5) from “Global InsuranceTelematics Study, 2012” by PTOLEMUS, that predicts the UBI market growth until 2020, by insurancemarket share.

Figure 1.5: Market size - share of the telematics-enabled policies in Europe and the US [11]

The US market will have more than 25% of its auto insurance revenue generated via telematics, growingto a total of $30 billion. The European market is forecast to represent more than AC50 billion.

A Croatian company, Amodo, published an initial research outcome in 2013, showing that values of thedriving parameters related to risk of accident, decreased on average by 38% and a significant improve-ment of driving behaviour could be identified at 70% of the study participants [12].

As seen in Llaguno and Harbage [13], the main concern with this insurance system for the surveyparticipants is the possibility of privacy infringement, by having their driving locations monitored andrecorded and also with the possibility of data being shared with third parties, only surpassed by concernswith a possible premium increase. As a possible counterbalance, as already mentioned, the insurancecompanies usually offer the possibility of GPS tracking in case of theft, automatic position signalling incase of accident and feedback on the driver behaviour.

Table 1.1 presents a summary of the previous information, listing all the mentioned services and thetechnologies in use by each one of them.

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Company Service name Country Year FeaturesProgressive Corporation Snapshot USA 1998 Records hard braking events,

time of driving and mileage; noGPS information is taken

GMAC Insurance OnStar USA 2004 Counts total mileage drivenAll State Drivewise USA 2012 Records speed, mileage, time of

driving and braking; the clientcan have online access to in-formation about his driving be-haviour

Ok! teleseguros Ok! GPS Portugal 2012 Records speed, distance, hoursof driving and driving location;GPS signal can be used in caseof theft or need of assistance;accident signal if over 2,5g

Table 1.1: Summary of mentioned UBI service providers

1.2 Objectives

Taking into account all the introductory information presented in Section 1.1, it becomes clear that theability to objectively evaluate comfort and safety conditions is of interest for a specific industry. Accom-panying that interest, technological evolution has put us closer than ever to that goal, something that isproven by the number of players in the insurance business trying to make use of this technology.

The perspective of fast development of On Board Units and its capability to collect data lead to twofundamental questions that the work tries to answer:

• Is it possible to evaluate comfort and safety conditions of the occupants of a light duty vehicle justby taking into account dynamic variables, such as acceleration?

• If possible, are the methods in use by other authors valid for practical application?

The best way to answer the first question is by gathering as much information as possible concerningmethods and evaluation criteria from articles, previous experimental studies and international standardsto validate the idea of performing the mentioned evaluations.

In order to answer the second question, the use of an available On-Board Unit is required, providing thecapability to collect all the data concerning any events of interest in normal day driving. The collecteddata shall then be used to implement the calculation methods needed in order to validate and compareany results.

In order to develop the work, the use of the On Board Unit will follow the steps presented in the followinglist:

• Track a vehicle in a real scenario, to collect data related to the dynamic behaviour in normal drivingconditions

• Establish criteria for the evaluation of comfort and safety conditions

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• Develop a computational model to assess the comfort and safety conditions in each moment

The ultimate goal of this work is to develop a state machine that provides information on both safety andcomfort conditions of the vehicle occupants every second.

All these steps and the objectives of this work will be fulfilled using a short sample based on one vehicleand one, maximum two drivers. This of course means that any of the methods that will be presented inChapter 2 requiring the use of very large samples of drivers are out of question for implementation. Itgoes without saying that methods based on vehicle observation or evaluation from the outside are alsointeresting to mention to contextualize but not to implement since that would go against the idea of usingan On-Board Unit.

When available, guidelines previously established by international standards shall be used and subjectto the same critical evaluation.

As a final consideration, the opinions of the trials’ participants shall be taken into account whenever thereare doubts about the validity of the results, in order to evaluate the accuracy of the available guidelinescompared to the perception of the vehicle occupants. Due to the small size of the sample, these opinionswere obtained during informal conversations without any kind of questionnaire being prepared for suchpurpose.

1.3 Structure of the Thesis

The thesis is divided in seven chapters:

The first chapter features a short introduction, explaining the motivation for the development of this work,followed by a list of questions that are intended to be answered, the objectives of the thesis and a shortexplanation on the structure of the thesis.

In the second chapter, a summary of the current state of the art is presented, including all the informationconcerning the evaluation of comfort and safety and new areas that have potential for future developmentwith the use of on-board monitoring techniques.

The third chapter of the work is focused on the methodology, divided in two parts: data collection andgeneric calculations. In the data collection section the apparatus is presented, namely the componentsand characteristics of the OBU (On-Board Unit), followed by the experimental procedure. The genericcalculations section is focused on equations that are used throughout the rest of the work. This includesthe presentation of the conversion from GPS coordinates to a distance between two points, numericalanalysis methods and the signal filtering.

The fourth chapter is dedicated to dynamics. More precisely, this chapter is focused on trying to correctand further improve the quality of the data related to speed obtained from the vehicle, by combining itwith information from the accelerometer in order to improve the resolution.

The fifth chapter is focused on comfort, being based mostly on using the international standard ISO2631-1, its applicability and possible problems, including specific events that are used as examples.

In the sixth chapter, the method used to evaluate safety is explained, including the choice of the refer-ence values being used and a discussion on the effectiveness of each of the different methods used tocalculate safety conditions. Again, specific events are presented as examples.

8

The seventh and final chapter is where conclusions relative to the accuracy and reliability of the appliedmethods are presented, especially concerning some specific situations, summarizing the conclusionsfrom the previous chapters. Future developments are also discussed, with suggestions on how to im-prove the three focused areas of dynamics, comfort and safety and possible new applications for theOn-Board Unit.

9

Chapter 2

State of the art

According to Faris et al [14], ride comfort is defined as how a vehicle responds to road conditions orinputs other than its occupants. Until now, most of the testing for ride comfort has been done in asubjective way as there is not an objective testing developed in a standardised way. Most research inride quality focus in one of three areas: human response to vibration, vehicle response to excitation andways of testing and evaluating the ride quality [14].

Temperature feeling, feeling of air speed in the cabin, the feeling of vertical acceleration, the feeling oflight type and intensity in the interior and of course ergonomics, are the main factors on which a humanbeing bases its opinion on comfort [15].

The work of Smith et al [16] provides good insight into what to evaluate and how to evaluate comfort con-ditions. The method used in this work was based on both subjective and objective measurements. Forthe subjective part, ratings by the car passengers were used. For the objective part, acceleration mea-surements were used, including both vertical and lateral floorboard and lateral seat/passenger interfaceaccelerations.

The study compares passenger ratings with ISO standards, UTACV (Urban Tracked Air Cushion Vehicle)Specification, a boundary below which the power spectral density of the ride accelerations must be atall frequencies. For the development of weighting functions, the two previous methods and also theAbsorbed Power method of Lee and Pradko, that relates the comfort condition to the average powerabsorbed by the passenger, calculated as a weighted integral of the acceleration spectral density [16],were used.

For the UTACV the spectral density was calculated, with the result being that all the automobile rideshad spectra that exceeded the UTACV boundaries at some frequencies, indicating that the boundarymight be too conservative. For the comparison with ISO boundaries, the RMS acceleration was used.Most of the ratings were rated as smooth.

Each of the methods selected by the authors allowed for the development of a weighting function. Thesewere compared with the “unweighted” RMS value of the ride acceleration (equivalent to using a functionthat gives all frequencies the same weight), showing that the “unweighted” versions were not worsepredictors of ride quality than the weighted ones, contrary to what was expected.

A statistical study shows a linear correlation between the weighted indices and the mean personalratings. The authors then proposed two equations, one for comfort and another one for discomfort. For

11

the case of comfort, if the magnitude RMS index is used, the same linear equation can be used for bothfloorboard and seat vibrations. The resulting least squares fit equation is:

R = 5, 43− 40, 0α (2.1)

where α is the magnitude RMS acceleration at either the floorboard or the seat and R is the ride rating.Since the previous equation results in a measure of the ride which decreases as a function of the levelof vibrations, it makes sense to convert to the form of a discomfort equation such that

D = 5, 0−R (2.2)

where D is defined as the discomfort of the ride. Substituting Equation 2.1 into Equation 2.2,

D = −0, 43 + 40, 0α (2.3)

where D varies from 0 (“the best ride you can imagine”) to 5 (“the worst ride possible”) [16].

The evaluation of the vibrations can be done using unweighted acceleration spectra for floor or seatdata in the vertical and transverse directions. A magnitude of the RMS values defined as the squareroot of the sum of the square of the vertical and lateral RMS acceleration is recommended for either ofthe locations. The values of these magnitude weighted RMS values will range roughly from 0 to 0,04 gfor smooth (interstate highway) rides, 0,04 to 0,06 g for medium rides, and above 0,06 for rough rideswhich could be used to predict statistically general passenger rating of the ride [16].

The research done by P.S. Els [17], intended to find out which was the best objective method to evaluatecomfort in use. The comparison was done with ISO 2631, BS 6841, Average Absorbed Power and VDI2057. The ISO 2631 standard is used mainly in Europe and the British Standard BS 6841 in the UnitedKingdom. Germany and Austria use VDI 2057 while Average Absorbed Power or AAP is used by theUnited States of America and by NATO in the NATO Reference Mobility Model. To perform the tests, amilitary vehicle was driven over different terrains, chosen to excite significant amounts of body roll, pitchand yaw motion, using various vehicle speeds and tyre pressures. Aside from the data monitored andrecorded during the test, all participants were asked to answer a questionnaire to evaluate the correlationbetween the threshold values of each standard and the participants feeling.

As a result of the analysis, it was concluded that the vertical acceleration gives the best correlationbetween subjective and objective ride comfort values. The respondents experienced pitch and roll of thevehicle body as vertical acceleration [17].

Research by Emmanuel Leon Felipe [18] addressed the problem of highway design, more specificallythe building of horizontal curves. For a first experiment at the Pacific Traffic Education Center, a videocamera and a three-axis accelerometer were used to ride in two courses, a closed eight loop and afigure “S” loop. Two experienced drivers were required to drive at the maximum safe speed before thecar started to skid. For all the drivers, a comfortable or an easy ride is under the region of 0,4g of lateralacceleration. At the same institution, in 1992 the Royal Canadian Mounted Police performed a skid test,with three different cars, obtaining a mean value of 0,84g for longitudinal acceleration. The distribution ofthe load on the four cars’ tyres is different when braking than when cornering. Therefore, the skid patternwhen cornering, happens usually with a lower lateral acceleration ay than the longitudinal acceleration

12

ax needed to skid when braking hard. In this case, the value of ay can be approximated by ay = 0,9ax.

The tests at the loops were performed under two different scenarios, characterised by the speed. Sce-nario V1 corresponded to comfortable speed and V2 to fast speed. Each was driven by two expertdrivers and two groups of four regular drivers. The conclusions include information on the “trade-offbetween speed and lateral acceleration:

• For scenario V1, people tend to drive at a speed which corresponds to a lateral acceleration of0,35 - 0,40g in sharp curves. However, on flatter curves, the lateral acceleration seems to no notinfluence the speed selection. The drivers selected their comfortable speed mostly based on thespeed itself.

• For scenario V2, the regular drivers’ speeds in sharp curves are almost the experts’ speeds. Thegap between the two set of speeds increased thereafter with the increase of radius. Regulardrivers, adjusted their maximum ’safe’ speed in flatter curves mostly based on the speed itself.” [18]

Another study [19] on comfort was done by evaluating how effective speed bumps are in inducing dis-comfort on passengers. For this work, the apparatus included a biaxial accelerometer (fixed on theoutside of a car door) connected to a USB data acquisition system with a 100 Hz sampling frequency.The experimental procedure included several drives over a speed bump, at four different speed levels,two of them lower than or equal to the local speed limit and the other two above the speed limit.

For data processing, to eliminate noise present during the data acquisition, every five consecutive datapoints were averaged giving an effective measuring rate of 20 Hz. These points were then integratedapplying the root mean square method for the vertical acceleration. During the study, it was noticedthat longitudinal acceleration was present when the vehicle impacted a speed bump but in negligibleamounts and so its effect was neglected. It was concluded that while going over a speed bump atspeeds exceeding the speed limit the passengers experience uncomfortably high vertical accelerations.Vertical acceleration increases exponentially with linear speed increase [19].

Regarding safety conditions, risky behaviour like inadequate speed or too short distances to the preced-ing cars show a relation with the number of conflicts and accidents in which a driver is involved [20].

The Swedish traffic conflicts technique was the result of research by Christer Hyden in 1987 [21] [22],where the detection of safety critical events is done based on two main factors: time to accident andconflicting speed. Time to accident is defined as, “The time that remains from one of the road usershave started an evasive action, until a collision would have occurred if the road users had continued withunchanged speeds and directions”. Time to accident is calculated based on distance and a conflictingspeed. With these data it is possible to classify each situation according to Figure 2.1.

13

Figure 2.1: Conflict graph with definition of serious conflict (TA: Time to accident) [22]

A serious conflict is characterised by suddenness and harshness in action of at least one of the involvedand for being a situation in which drivers say they would never like to be involved [22]. The maindrawback of this method is that it is based on trained observers evaluating the seriousness of conflictsin specific locations. On the other hand, the technique shows that conflicts and accidents belong tothe same process, just with different degrees of seriousness (most often), since the patterns are veryalike [22]. Figure 2.2 shows the scale used to classify the different level of conflicts.

Figure 2.2: Continuum of traffic events from undisturbed passages to fatal accidents, as originally de-veloped Christer Hyden in 1987 [23]

Finally, the definition of conflict used by the author implies that two road users must interact during aconflict.

14

Other studies based on in-car observations have been done, like the ones by Hjalmdahl and Varhelyi [24]and Oscar Siordia [25]. In the former study [24], a car was equipped with three video cameras and dataloggers, registering the distances to both the front and the back. A laser radar was also used to measurethe distances between cars and a GPS signal was used to register information about time, positioning,speed and speed limit, sampling at a frequency of 5 Hz. Three individuals with basic traffic engineeringknowledge were given training and then asked to analyse the risk level achieved by different drivers ona test route. A disadvantage of this method is the influence of the environment on the observers, as forexample in a test performed during rush hour in the afternoon (test number 4), where the results werevery poor, indicating that fatigue due to a bigger amount of observation events plays an important role inthe reliability of this method, as can be seen in Figure 2.3. The results show that the method is valid but ismuch dependent on the observer conditions, such as a situation in which one of the observers oversleptin the morning and then performed badly during the test or the situation mentioned before about theeffect of fatigue. In the latter [25], data was acquired in a highly realistic truck cabin simulator, withsessions performed in four different scenarios. Data obtained included registers of the vehicle dynamicsand road characteristics and visual information obtained from two cameras, one of the driver’s top viewand the other of the simulator main view. Then, a visual analogue scale from 0 to 100 was establishedfrom the classification of three experts and then five data mining algorithms were trained to predict thedriving risk level, based on driver, vehicle and road information, with all tests being performed in a truckcabin simulator. In this case, the main problem found was that since the algorithms were trained usingfour different environments (urban, mountain, interurban and circuit), when trying to use an algorithm toevaluate a different environment the performance was very poor.

Figure 2.3: Scores for the three observers [24]

The research of van der Horst [23], used recordings of traffic conflict situations. The procedure consistedin making video recordings with one or more fixed cameras on the spot and an offline analysis. By usingreference points in video stills it was possible to translate the x- and y- coordinates into the road plane.From the analysis of consecutive video stills, it was possible to calculate speed, acceleration, time tocollision and heading angle. The work resulted in the conclusion that in normal situations, only takinginto account situations related with the “static” environment, braking goes up to -4 m/s2 (for example, ata railway crossing).

Further analysis shows that for hard braking the limit goes from -6,5 m/s2 up to about -7,5 m/s2 and fornormal braking goes from approximately -4,0 m/s2 to -5,5 m/s2, depending on the speed. These results

15

can be seen in Figure 2.4. This last experiment, called “The Vancouver Experiment”, was performedwith the subjects driving at three different levels of speed, wearing liquid crystal glasses that could becontrolled to suppress visual information. The drivers should brake only after being given an order to doso, braking either normally or hard while feeling that the distance to an obstacle was still safe to avoidhitting it. To register data, an on-board computer was used, registering speed and distance by countingimpulses in a Hall transducer mounted on the driveshaft with the status of ten binary input lines at asample rate of 10 Hz. One binary input line was connected with a switch on the brake pedal and anotherwith a pulsed beam infrared detector that fired at the moment each one of the two reflector poles werepassed. These registration enabled the measurement of distance travelled with time, longitudinal speedwith time, moment of initiating braking action and moment of passing reflector poles [23].

Figure 2.4: Effects of braking instruction and speed on the mean maximum deceleration as reachedduring the control of braking [23]

It must be noted that the occlusion referred in Figure 2.4 is related to the methodology used in [23],where the driver’s vision was affected and it is considered to be out of the scope of this work.

Research by Timo Lajunen [26], tried to find a relation of speed and acceleration as measures of thedriving style of young male drivers. For data acquisition, the subjects drove a test vehicle equipped withtwo video cameras (one pointing straight ahead and another point to the right side). The pictures weremixed into the same video screen, overlaid with VGA graphics with digital data from acceleration sensorsin the car and controls and stored on a videotape. The use of controls, speed and two accelerations(lateral and longitudinal) were also stored on a computer file at 15 Hz.

To evaluate the behaviour at specific locations, the authors analysed the behaviour on a crest and inboth gentle and sharp curves. While evaluating site-specific measurements, it was concluded that ona crest there was no relation of the behaviour with experience or accident involvement. As for thecurves, in the sharp curve prior accident involvement was related with the maximum acceleration used,as drivers with more accidents tend to use higher accelerations. The same relation was not found in thegentle curve. In the case of speed, in the gentle curve there was no relation with accident involvementor experience, while for the sharp curve there was a clear relation between higher speeds and prioraccident involvement.

A two-way analysis of variance (accident involvement vs. driving experience) indicated that neitherprior involvement in accidents nor driving experience were related to any effects on the longitudinal

16

acceleration or deceleration. The same analysis performed for lateral acceleration, showed that thedrivers with more than one accident use higher left accelerations (in right-sided curves). In the case ofexperience, there was no relation with the use of higher or lower lateral acceleration.

The two-way analysis of variance showed that there is a high relation between the use of higher speedsduring the tests and prior accident involvement while there is no relation between experience and speed.In the case of equivalent vector acceleration (the vector sum of all the weighted acceleration compo-nents), the two-way analysis showed a relation between higher equivalent acceleration and prior acci-dent involvement.

The authors concluded that maximum speed is a better way to gauge safe driving style than accelerationsignature or other quantities derived from it. Anyway it is considered that site-specific measures andacceleration signature should not be completely abandoned [26].

In [27], important empirical studies on speed and crash rate were reviewed. The review included self-report and case-control studies, with all the studies concluding that the crash rate increases with theincreasing of speed. Two of the referenced authors go even further, concluding that with increasingspeed on urban roads, the crash rate increases faster than on rural roads (Figure 2.5). In the authorsopinion, the best results are provided by the reviewed case-control methods, meaning that crash rateincreases exponentially for individual vehicles that increase their speed and increases faster, with aparticular increase in speed, on minor/urban roads than on major/rural roads. This makes clear the factthat in roads designed for lower speeds an increase in speed causes a faster raise in crash rate thanincreasing speed in roads designed for higher speeds.

Figure 2.5: Relation between speed and crash rate on urban 60 km/h and rural 100 km/h roads. Thefigure refers to a self-report study, developed by stopping drivers considered as fast or slow accordingto the traffic speed distribution and asking about their crash history [27]

Aarts and van Schagen [27] also reviewed the influence of speed differences at road section level, withresults showing that increasing average speed results in an increase of the crash rate for each of theroad types presented in Figure 2.6.

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Figure 2.6: Relationship between average speed and crash frequency on four urban road types [27]

In 1987, Christer Hyden suggested that by studying fluctuations in the acceleration and decelerationprofiles it is possible to detect possible safety critical behaviour [28]. During normal day driving, a driverneeds to brake regularly in a controlled manner, in a range of situations that goes from achieving slightspeed reductions using light engine braking to very powerful braking, in order to stop the vehicle as fastas possible.

Evidence was found that jerks or suddenness of braking can be used as a measure of safety criticaldriving behaviour. The study involved over 200 passenger cars equipped with an Intelligent SpeedAdaptation (ISA) system and data loggers, which recorded driving data such as the actual speed ofthe vehicles with a 5 Hz sample rate by means of a CAN (Controlled Area Network) bus. The ISAfunctionality uses the speed and position of the vehicle to inform the driver about speed limits, and togive warnings if the driver exceeds the speed limit, by comparing the driving data with the digital roadmap incorporated in the ISA equipment [28]. This data was then analysed to compare the drivers’self-reported accident involvement with the recorded jerk rate.

The authors also show some concern with problems caused by the used sample rate being too low,stating that in future trials the sampling frequency should be at least 10 Hz, preferably 20-50 Hz in orderto decrease the risk of sampling distortion, due to noise, that could otherwise affect the data during thenecessary smoothing and filtering of the raw data [28].

Further development was achieved by Bagdadi and Varhelyi. In this work [29], the problem of falsedetections of critical situations was addressed, based on the fact that safety critical situations need tobe distinguished from powerful but controlled braking. While braking, the vehicle is subject to negativeacceleration which tends to diminish as the speed is reduced. The magnitude of a jerk is greatly in-fluenced by the rate at which the acceleration begins and how it is carried out. The more abruptly thebraking starts and how the rate decreases, the more powerful the produced jerk is. In spite of accel-eration being strictly negative while braking, a jerk can have positive and negative values, independentof the final condition of the vehicle. An example of how jerks and acceleration are measured during abrake manoeuvre can be seen in Figure 2.7.

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Figure 2.7: Illustration of measures of acceleration (g) and jerk (g/s) during a brake manoeuvre [29]

To perform the tests, an Event Data Recorder (EDR) was developed, capable of measuring both longitu-dinal and lateral variations in acceleration. Data was continuously monitored by the EDR and recordedonto a hard drive for a predetermined time period before and after the occurrence of safety critical driverbehaviour. The main parts of the EDR were the data acquisition unit and a dual-axis accelerometer, asseen in Figure 2.8. The sampling frequency in use was higher than the limit suggested in [28], being setat 100 Hz. In order to calculate jerks based on measured acceleration data it is important to reduce thenoise as much as possible. Problems caused by noise were addressed by testing a few different filters,with Savitzky-Golay being considered the one with the best performance.

Figure 2.8: A conceptual description of the event data recorder [29]

The main goal of this project was to develop a new method, named critical jerk method. During thepilot study, a visual analysis suggested a jerk value of approximately 1,5 g/s for critical situations whilea threshold value of approximately 1,0 g/s is sufficient for detecting potentially critical events. Thesevalues were later confirmed by performing a naturalistic driving study [29].

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Commercially available equipments use much lower longitudinal acceleration threshold values. Twoexamples are the values used by Geotab Inc. and Autel Company. The first company, a leader intelematics usage for fleet management, in [30] presents the acceleration threshold values used. In thecase of deceleration, under default conditions, the company suggests a value of -4,76 m/s2 to define aharsh longitudinal deceleration. The same value (absolute value) is considered for lateral acceleration.In the case of Autel, in the Maxi Recorder User’s Manual [31], two different levels are defined, hard andextreme braking. These values can be changed using the available software, but the default values are-3.4 m/s2 for hard braking and -4.91 m/s2 for extreme braking.

Tables 2.1 and 2.2, summarize the threshold values obtained for both comfort and safety from variousauthors.

Magnitude (m/s2) Lat. Acceleration (m/s2)Craig C. Smith, 1976 0,59

Felipe, 1996 3,92

Table 2.1: Threshold values regarding comfort conditions

Lateral LongitudinalAcceleration(m/s2) Acceleration(m/s2) Jerk(m/s3)

Nygard, 1999 -9,90 to -12,60van der Horst, 1990 -6,50

Bagdadi and Varhelyi, 2013 9,81Felipe, 1996 7,42 -8,24

Autel Company, 2009 -4,91Geotab Inc., 2011 4,76 -4,76

Table 2.2: Threshold values regarding safety conditions

As the tables show, especially when the concern is safety, acceleration values vary widely. More specifi-cally, values obtained from academic research almost double the values of acceleration used by compa-nies that provide fleet management services. Possible explanations for these variations are the differentapproaches used to solve the problem, either during data collection or analysis methodology, or the goalof each work, as all of the referenced values apart from those used by fleet management companies,were obtained trying to analyse safety critical events. The use of equipment or vehicles based on oldertechnologies can possibly justify the higher thresholds obtained in the 1990s.

The values used as default by the two fleet management companies are more conservative than pos-sible, as both let the user change the parameters and Geotab mentions a ”too forgiving” level with adeceleration threshold of -5,64 m/s2.

A noteworthy use of EDRs, already outside of the scope of this work, is the study of injury risk ina collision with roadside hardware. The work developed by the authors of [32] intends to validate thecorrelation between Acceleration Severity Index (ASI) and the potential for occupant risk in crash events.

ASI(t) =

[(axax

)2

+

(ayay

)2

+

(azaz

)2] 1

2

(2.4)

20

Equation 2.4 is represents the method for the computation of ASI, where ax, ay and az represent the50 ms averaged component vehicle accelerations and ax, ay and az correspond to threshold values foreach component direction.

The experiment included following vehicles with an EDR installed, leading to the collection of data fromcollisions of more than one thousand cars. The data stored in a database included seat belt status forthe driver, airbag trigger time and longitudinal velocity vs. time sampled at 10 ms intervals during thecrash. Pre-crash the collected data included vehicle prior to impact, engine throttle position and brakestatus for five seconds preceding the impact.

In order to narrow the crash events to be analysed, a list of criteria was established to select eventsamong the whole database:

• Airbag deployment (according to the authors, the velocity change threshold is approximately 5 m/s)

• Recorded EDR velocity data

• Available injury data for either the left or right front seat occupant

• Belted occupants only

• Comprised of a single impact only

• Frontal collision

• No vehicle rollover

These criteria resulted in a sample of 120 different cases. In order to validate the use of EDR data, aftercalculating the ASI value with the EDR data the results were compared to six NCAP tests. Consideringthe NCAP results as correct, all of the cases resulted in an error of less than 10% in the ASI value.

The final objective of the work was quantifying occupant injury. For these purpose, the authors used theAbbreviated Injury Scale (AIS) and considered that an injury corresponding to the recommended limitfor ASI of 1,0 is equivalent to an AIS injury of level 1 or less. The Abbreviated Injury Scale is representedin Table 2.3:

AIS value Injury characterization0 No injury1 Minor2 Moderate3 Serious4 Severe5 Critical6 Maximum/Fatal

Table 2.3: Abbreviated Injury Scale levels

The authors concluded that there is a correspondence between the recommended limit of ASI 1,0 andthe condition of ”minor/no injury” and that ASI, with respect to the currently in use thresholds, is a goodindicator of occupant injury for belted and airbag restrained occupants involved in frontal collisions [32].

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International ISO Standard 2631-1

As mentioned before, ISO 2631-1, under the title “Mechanical vibration and shock - Evaluation of humanexposure to whole-body vibration”, establishes criteria for the selection of the method of measurementand analysis of the vibration environment and presents an approach to the application of the results [33].Also, with the objective of raising awareness of the complexity of human physiological/pathological re-sponse as well as behavioural response to vibration, some guidance on the effects of vibration on health,comfort and motion sickness is given.

One must be aware that in case of extreme-magnitude single shocks, such as in case of vehicle acci-dents, the methods used are not applicable.

Since this work is focused on safety and comfort in a car, both driver and passengers will be in a seatedposition and so, the following figure applies in terms of position and axes:

Figure 2.9: Basicentric axes of the human body - seated position [33]

The international standard states that vibration shall be measured according to a coordinate systemoriginating at a point from which vibration is considered to enter the human body. The location ofmeasurement shall be in an area of contact between the body and the vibrating surface. As seen inFigure 2.9, for a seated person the main contact surface for measurements are the supporting seatsurface, the seat-back or the feet. Regarding the signal conditioning, the only option given by the normis to use a low pass filter if needed.

The primary quantity to measure comfort conditions is acceleration.

The basic way to evaluate acceleration is through the weighted root mean square method, expressed inm/s2, defined by Equation 2.5

aw =

[1

T

T∫0

a2w(t)dt

] 12

(2.5)

22

Where aw(t) is the weighted acceleration as a function of time, in m/s2, and T is the time of the measure-ment, in seconds. Depending on the location of the measurement points, different weighting factors shallbe applied for the calculation of acceleration values.The frequency weighting factors are dependent onthe band of vibration frequencies in which the vibration is measured. For calculations, one shall selectthe weighting factor, Wi, according to the band of frequency to which the system is subject. Also, to cal-culate the RMS acceleration for the different situations (health, comfort or motion sickness), a multiplyingfactor is defined for each axis, taking into account the position of the subject.

In a situation where the vibration exposure consists of two or more different magnitudes and durations,the following equation applies:

aw,e =

(∑a2wiTi∑Ti

) 12

(2.6)

Where aw,e is the equivalent vibration magnitude (RMS acceleration in m/s2) and awi is the vibrationmagnitude (RMS acceleration in m/s2) for the time of exposure Ti.

In case of a need to combine accelerations in various directions, the total value can be calculated asfollows:

av = (k2xa2wx + k2ya

2wy + k2za

2wz)

12 (2.7)

Where ki are multiplying factors, defined in accordance with the position of the passenger and the loca-tion of the measurement equipment.

Comfort

As mentioned before, the text for ISO 2631-1 establishes guidelines for the values of the magnitude ofacceleration to evaluate the effects of vibration on comfort and perception in public transport.

Magnitude (m/s2) Comfort Level≤0,315 Not uncomfortable

0,315 to 0,630 A little uncomfortable0,500 to 1,000 Fairly uncomfortable0,800 to 1,600 Uncomfortable1,125 to 2,500 Very uncomfortable≥2,000 Extremely uncomfortable

Table 2.4: ISO 2631-1 comfort guidelines

Health

Concerning the effects of vibration on health, although this is done in a very basic way due to a lack ofdata to establish a relation between vibration and the effects on health, the norm still provides guidelinesfor the assessment of health risk. Due to very limited experience in the application of ISO 2631 to assess

23

health in most of the body positions, the guidance provided is based almost entirely on research thatonly takes into account the z-axis of a seated person.

Basic health risk can be assessed by two different equations. Both of them define when two dailyvibration exposures are equivalent:

aw1 ∗ T121 = aw2 ∗ T

122 (2.8)

aw1 ∗ T141 = aw2 ∗ T

142 (2.9)

These two equations are used to draw the plot in Figure 2.10. As seen in the figure, both equationsestablish an upper and a lower limit of acceleration depending on the time of exposure.

Figure 2.10: Health guidance caution zones [33]

The shaded area, between 4 and 8 hours is the one for which most occupational observations exist.

This section of ISO 2631-1 is applicable to situations where the body is exposed to long-term high-intensity vibration, while at work, travelling or during leisure activities. The main concerns are risks forthe lumbar spine and the nervous system, that are generally affected only after years of exposure.

Motion Sickness

Motion exposure for long periods of time may lead to symptoms of motion sickness. It is possible for asubject to adapt in case of exposure for very long periods (e.g., a few days) and even some adaptationcan be retained for future situations.

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The international standard presents two options for the measurement of the motion sickness dose value(MSDV).

If possible, the MSDV shall be determined from measurements taken throughout the full period of expo-sure. It can be calculated by:

MSDVz =

[ T∫0

(aw(t))2dt

] 12

(2.10)

where aw(t) is the frequency-weighted acceleration in the z direction and T is the total period duringwhich motion could occur, in seconds. This calculation gives a result in m/s1,5.

If the exposure is continuous and has an approximately constant magnitude, the MSDV can be estimatedfrom the frequency weighted RMS determined over a short period. For the exposure duration T0, theMSDV can be calculated by

MSDVz = awT120 (2.11)

In case of using Equation 2.11, the measurement period should not be less than 240 seconds.

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Chapter 3

Methodology

This chapter is divided in two parts. In the first part, the data collection procedure is explained withdetails on the On-Board Unit use and the selected route. The second part is focused on calculationsthat are necessary for the proceeding chapters and data filtering.

3.1 Data Collection

The development of this work required the collection of data from real scenarios, using an instrumentedvehicle. In order to collect data the On-Board Unit was used, along with a laptop computer and, for thesecond trial, a video camera.

For the experimental procedure a route including as many comfort and safety related events as possiblewas selected in an urban setting in the Lisbon area.

3.1.1 Apparatus

The apparatus to perform the experiment consisted of a common hatchback vehicle, an On-Board Unitfor data recording and a laptop with Bluetooth communication capability.

The On-Board Unit is basically a small box, encasing a three-axis accelerometer, a Bluetooth module, aGPRS module, a SD card holder, a GPS signal receiver and a barometric altimeter. The OBU is poweredthrough the OBD port connection [34]. The apparatus can be seen in Figures 3.1,3.2 and 3.3.

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Figure 3.1: GPS receiver antenna

Figure 3.2: The inside of the OBU

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Figure 3.3: OBU installed in the vehicle

The positioning chosen for the OBU is related with the ISO 2631-1 standard. As seen in Chapter 1, themeasurements for comfort shall be made on one of these positions: feet, seat-back or supporting seatsurface. Of these three possible locations, the decision was made to place the OBU as close as possibleto the supporting seat surface to allow for an easier setup of the equipment while avoiding discomfort forthe driver.

Only during the second trip, a video camera was added to the apparatus in order to ease further analysis.

3.1.2 Experimental Procedure

For the development of this project, it was needed to perform an experiment where all the events ofinterest that can happen during a normal car travel would be included. For this, a route was selected,comprised of relatively well-known urban and suburban roads in the Lisbon Area. As will be explainedlater on, some problems arose during the first trip, creating the need to repeat the experimental proce-dure in a second trip. For the second trip, as a way to maintain the conditions as close as possible tothe first time to allow for the direct comparison of the data obtained, the same route was used wheneverpossible.

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Figure 3.4: Map showing the route used during the tests

The events mentioned in the previous paragraph were all, as said before, representative of commonsituations that can happen to any driver during normal day travelling. The events that were included atleast once during the trips and to which a marker was attributed in the data recording, were: acceleration,braking, right turns, left turns, roundabouts, lane changes, potholes, speed bumps and cobblestonepavement. The markers were introduced as user inputs though a laptop computer during the trips andrepresent a feature that is only available for the purpose of testing and not for use by a final consumer.

For the first trip, taking into account all the information gathered from previous works, it was decidedto setup the accelerometer sampling frequency at 100 Hz. This value was the highest found as asuggestion in the whole bibliography review. Also, for data transmission, it was decided to use theBluetooth connectivity saving the data directly to the computer. During the first test, the input of themarkers revealed itself to be fallible due to a delay of three to four seconds in relation to the time ofinput. After this test, some problems were found while analysing the data as a few parts of the collecteddata were unusable due to incorrect registration to the text file. The probable cause for the issuesconcerning the input of markers and file writing was the use of the Bluetooth connectivity while writingto a file at the same time, requiring the use of too many resources at once. The visible effects of thisproblem in the text file were lines with half the information available, lines overwritten with other linesand most of all, the incapability of the OBU to keep the data transmission at the intended frequency.The problems were exacerbated whenever more intense events happened during the trip, such as hardbraking or travelling through cobblestone pavement.

For the second trip, the same sampling frequency was used. The difference in the setup was the savingof the data directly to the SD card placed inside the OBU. This proved to be a good decision, as theresulting files were almost 100% problem free, except for a whole second that was not registered whenthe car engine went off after an extreme braking situation. The only technical problem found during thetrip was that the OBU sometimes tried to connect to a server for data transmission, which was making itimpossible for the user to introduce the markers to identify the events for a few seconds. This problemwas solved remotely during a short stoppage.

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Concerning the collected data, as already mentioned, acceleration was sampled at 100 Hz in the threeaxis. Other information collected during the trips included latitude and longitude from the GPS andspeed information from the OBD port, all of them with a sampling frequency of 1 Hz. The capability fora user to insert markers during the trip while using a laptop was also used, in order to allow an easieranalysis later on.

As mentioned in section ??, for the second trial a video camera was used with a vehicle occupantoperating it. Later on during the data analysis, the availability of the video represented a helpful additionas any doubts on the kind of event that led to variations in the collected data were easily dissipated orwhenever the perception of the participants did not match the results.

3.2 General Calculations

During the experimental procedure data was collected from the OBU and the vehicle itself. While themain idea of this project was at first to determine how a passenger perceives comfort and safety condi-tions in a light-duty vehicle using the information obtained with an accelerometer, questions were raisedconcerning the possibility of some occurrence that would lead to accelerometer data unreliability, be ita malfunction or incorrect positioning of the OBU. This created the need to look for alternative ways tocalculate approximations of the quantities of interest.

Table 3.1: Differentiation and integration relations between distance, speed and acceleration

3.2.1 Distance Between Coordinates

Two of the recorded data were latitude and longitude. While both of them by themselves are not of greatuse, the combination of the two coordinates to calculate distances is quite useful.

As seen in Chapter 2, travelled distance is one of the most common criteria used by insurance compa-nies to determine the cost of an insurance policy, which leads to the interest of including in this project amethod to calculate the travelled distance. For this purpose, Equation 3.1 can be used to calculate thedistance on a sphere, with both latitude and longitude in radians:

e = arccos[sin(Lat1)× sin(Lat2) + cos(Lat1)× cos(Lat2)× cos(Long1 − Long2)] (3.1)

Finally, taking into account the radius of the equator, the final equation results in:

distance = e× 6378137 (3.2)

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where 6379137 is the radius of the equator in meters.

This method assumes a spherical shape for the Earth. It must also be noted that Equation 3.1 is onlyapplicable in locations in the northern hemisphere and west of Greenwich. As explained in [35], in orderto be used in the southern hemisphere or east of Greenwich, the coordinates have to be multiplied by-1.

3.2.2 Numerical Analysis

Due to the characteristics of the problem being studied, namely the impossibility of having a function thatprovides an exact solution for the sampled data, the use of numerical analysis is needed. The numericalmethods were used for both integration and differentiation and chosen according to the informationavailable in the literature. Also, while other parts of this project had standardized methods to performthe calculations, for the numerical analysis there is a huge amount of options to choose from.

During the selection process, care was always taken to make sure that the selected methods require aslittle computational effort as possible if these methods are to be implemented in an OBU, that has limitedcomputational power.

Numerical Differentiation

In order to obtain the first and second derivatives, the most widely accepted method is the Finite Differ-ence method. For the first derivative of either position or speed, it was used a first order central difference(Equation 3.3), which tends to provide a smaller error than progressive or regressive differences. For thesecond derivative, used only to obtain the longitudinal acceleration from the known position, the secondorder central difference method (Equation 3.4) was used.

Dhf(x1) =f(x1 + h)− f(x1 − h)

2h(3.3)

D2hf(x) =

f(x− h)− 2f(x) + f(x+ h)

h2(3.4)

where h represents the step (time interval between two values of f(x)). This value can be manipulated inorder to improve the results.

Numerical Integration

As mentioned previously, special care was taken with the computational effort of the selected methods.Due to this, the decision was made to stick to the basic rules, namely the Midpoint Rule, the TrapezoidalRule and Simpson’s Rule. While the lack of an exact solution makes it impossible to calculate thenumerical error associated with any option, all these three methods have been highly tested and so it isfairly easy to find articles or books where comparisons between the three methods are presented.

As can be seen in [36] and [37], examples with an exact solution clearly show that Simpson’s rulealways gives a result with a much smaller error than both Midpoint and Trapezoidal rules. In [37] it is

32

also explained why this much smaller numerical error is achieved with almost the same computationaleffort.

The definition of Simpson’s Rule is:

Ih(f) =x1 − x0

6[f(x1) + 6f(

x1 + x02

) + f(x0)] (3.5)

The derivatives are used in order to obtain values of speed from distance and acceleration from bothdistance and speed, while the integration method is used for the inverse operations.

3.2.3 Data Filtering

The collected data, in a raw condition, presents a very noisy behaviour. Possible sources of noise arevibrations of the plastic panel to which the OBU was attached ( 3.3), an accelerometer that does not havethe right noise specifications for the application or signal losses caused by data transmission throughcables. This leads to difficulties in analysing data, specially in the case of safety where points that areclearly wrong can lead to wrong conclusions, as that component of this work uses data without anyintermediate calculations.

The option to low-pass filter any accelerometer data, according to ISO standard 2631-1 is left at theuser’s discretion. One of the main characteristics of a low-pass filter is that short-term fluctuations areeliminated while the longer-term trend of the signal is kept, resulting in a smoother form of the signal.

Three different relatively simple filters were tested: a simple low-pass filter, a moving average and a low-pass Butterworth filter. In the case of the simple low-pass filter, many isolated peaks were compressedtoo much, what could lead to wrong results while evaluating safety conditions, as safety risk events tendto be short in time but with a very fast increase and decrease.

While comparing the results obtained with the moving average and Butterworth filters, it is relatively easyto see on Figure 3.5 that the results are very similar between the two of them.

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Figure 3.5: Comparison of the results applying the moving average and Butterworth filters to the longi-tudinal axis data. Raw data (blue) vs. Filtered data (red)

The final decision was in favour of using Butterworth filtering. While Butterworth filtering has the disad-vantage of introducing a small phase distortion and moving average filtering is much simpler and easierto apply, the Butterworth method allows for better control of its behaviour and more versatility for futuredevelopments, especially if a frequency domain analysis is carried out. In [38], a summary of the mainfeatures of the two filters is available:

Moving Average ButterworthMoving averages can indeed smooth noisysignals

Excellent passband response

They cannot separate out different frequencycomponents (cannot pick a cutoff frequency)

Arbitrarily sharp roll-off can be achieved by in-creasing filter order

Have poor (“gradual”) roll-off characteristicsIntroduces a phase (time) distortion intosmoothed data (but this can be easily cor-rected)

Have very poor stopband attenuationBasically is a sophisticated weighted, recur-sive, moving average filter

In most cases, there will be a better optionthan using a moving average

Table 3.2: Qualitative comparison of the main features of Moving Average and Butterworth filters

As mentioned in Table 3.2, the moving average does not allow to pick a cutoff frequency, which is crucialif there is interest in a frequency domain analysis. Also, being a more sophisticated version of thesame filter and the requirement of ISO 2631-1 to use a filter with Butterworth characteristics to establishband-frequency limits to apply frequency weightings, tips the balance in favour of the Butterworth filter.

Apart from the previously mentioned options, the Kalman filter is also a common option for accelerom-eter data filtering. Although being called a filter, the Kalman filter works more as a predictor, producing

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estimates of the variables while taking into account the respective uncertainties. This filter is very appre-ciated not only for academic purposes [39] but also among robotics communities, especially for positiontracking or when developing methods to stabilize quadcopters during the flight. The main drawbacksof this filter are requiring not only the availability of more data apart from the acceleration and also thedifficulty of implementing it on some hardware [40].

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Chapter 4

Dynamics

During the development of this project, questions were raised regarding the validity of both the speedindicated by the OBD and derived from the GPS coordinates and also the distance calculated from both.

Regarding the information obtained from the speedometer, one of the problems is related to and ex-plained by UNECE regulations, enforced by European legislation applicable to vehicle production. Whilein [41] a complete set of rules is established, taking into account all aspects related to a car speedome-ter, from the positioning of the equipment in the car to the marked scale, only the test conditions are ofinterest for this work. Table 4.1 includes the speeds at which the tests shall be performed [41]:

Maximum design speed (Vmax) of the vehiclespecified by the vehicle manufacturer (km/h)

Test speed (V1) (km/h)

Vmax ≤45 80 % of Vmax

45 <Vmax ≤10040 km/h and 80 % Vmax (if the resulting speedis ≥55 km/h)

100 <Vmax ≤15040 km/h, 80 km/h and 80 % Vmax (if the result-ing speed is ≥100 km/h)

150 <Vmax 40 km/h, 80 km/h and 120 km/h

Table 4.1: Speeds at which the vehicles are tested to test the speedometer accuracy

Combining with the values in Table 4.1, there are two conditions directly related to the speed informationgiven by the speedometer [41]:

• The speed indicated shall not be less than the true speed of the vehicle.

• At the test speeds specified in Table 4.1, there shall be the following relationship (Equation 4.1)between the speed displayed (V1) and the true speed (V2)

0 ≤ (V1 − V2) ≤ 0, 1× V2 + 4km/h (4.1)

Then, as an example, according to Equation 4.1 a vehicle at 80 km/h must have an indication no lowerthan 80 km/h and no higher than 92 km/h.

The OBD speed collected for this work already includes a slight correction, considering an approximationof the speedometer speed minus 3 to 4% of that same speed, since most manufacturers tend to include

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a margin that is within the regulations and so, above the real speed.

Another concern with the values obtained for speed is related with tire wear. The speed shown to thedriver is based on RPMs and an average tire radius, somewhere between the radius of a new tire andthe radius of a well worn tire. Taking into account this condition together with the application of theEuropean legislation and the corrections made for the OBD speed, the relation between the differentspeeds is probably as such:

VUsedTire ≤ VOBD ≤ VNewTire ≤ Vspeedometer (4.2)

Regarding the GPS receiver, the problems of the collected coordinates are related with possible con-nectivity problems caused by sources of interference. The United States government has an informativewebsite concerning the provision and maintenance of the system, where it is stated:

“The Global Positioning System uses radio signals in frequencies (spectrum) reserved for radio navi-gation services. Ensuring the continuity of the GPS service requires protection of this spectrum frominterference.

GPS interference can come from a variety of sources, including radio emissions in nearby bands, inten-tional or unintentional jamming, and naturally occurring space weather” [42].

The US Department of Defense also released a public document defining the levels of performancemade available to the GPS SPS users [43]. One of the chapters of the document is a list of all theerrors excluded due to being error sources not under direct control of the Space Segment or the ControlSegment. The following list specifies what errors are excluded due to effects of, being a bit more specificthan the previous quote:

• Signal distortions caused by ionospheric and/or tropospheric scintillation

• Residual receiver ionospheric and tropospheric delay compensation errors

• Receiver noise (including received signal power and interference power) and resolution

• Receiver hardware/software faults

• Multipath and receiver multipath mitigation

• User antenna effects

• Operator (user) error

The result of performance analysis also requires the horizontal accuracy to be in a range of ± 3 meters.

There are two other possible problems related with the calculation of distance using the GPS coordi-nates. One related with the approximation used by Equation 3.1, since this does not account for roadturns or altitude differences and the real behaviour of the car, using only straight lines to connect eachtwo coordinates and a different problem concerning incorrect positioning for a few seconds, resulting inslightly incorrect results, as seen in Figure 4.1, a problem related with the accuracy mentioned previ-ously.

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Figure 4.1: Coordinates slightly deviated from their real position

4.1 Methodology

The first approach to calculate speed and distance, as mentioned before was quite straight forward,applying directly the numerical methods presented on Chapter 3.2.2. Figures 4.2 and 4.3 show theobvious unreliability concerning the usage of acceleration integration to obtain any results, even in asmaller sample of the whole course. Acceleration was averaged in order to reduce the sample to afrequency of 1 Hz.

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Figure 4.2: Different speeds, including the integration of acceleration in a sample of five minutes

Figure 4.3: Different total distances, including the integration of acceleration in a sample of five minutes

The results for acceleration integration seen in Figures 4.2 and 4.3 are clearly unreliable due to drifting.This drift can be caused by the effects of noise not being completely removed or non-removal of bias(which was removed in this case). The integration error accumulates along time and in a double inte-

40

gration, the distance includes a double integration of the error, leading to the presented discrepancy. Amethod considered as a better approach to a numerical integration of acceleration in robotics communi-ties is working in the frequency domain and some research seems to backup the idea that results in thefrequency domain tend to be fairly accurate [44].

Another possibility is that the accelerometer itself leads to errors, as different accelerometers can leadto different magnitudes of error when integrating acceleration [45].

Since the results obtained by integration of the acceleration were totally unreliable, both of the situationswere discarded.

As seen in Figure 4.2, the speed obtained from the first derivative of distance gives an acceptable resultbut far from perfect. Situations like the one seen during the moments from approximately the 220th

second until the 240th, while clearly incorrect and unstable, have a mean value that is very close to theOBD value.

To address the concerns with the real vehicle’s speed, a method was developed to apply a slight cor-rection using the OBD speed and the 1 Hz sampled acceleration ( 4.4). The method involves in a firstmoment counting only the OBD speed as an initial guess and from the next second onwards, the medianof the new OBD speed with the previous value plus the acceleration registered during the last second.The GPS distance derivative was considered as unreliable due to the negative influence in the dynamicspeed prediction, caused by unstable signal transmission.

Figure 4.4: Flowchart of the dynamic speed prediction model

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The idea with this process is to dilute possible errors from the speedometer information caused by the1 Hz sampling frequency by taking into account the acceleration registered every second, in order toproduce a smoother and more reliable behaviour of speed by considering the car behaviour duringthe time between two speed samples. Another improvement is the resolution of speed, since the dataobtained from the OBD is composed only of integer values with a resolution of 1 km/h, while the appliedcorrection allows for at least two decimal places.

To calculate the total distance, the same process as before was used, integrating the dynamic speedwith Simpson’s Rule.

4.2 Results

After applying the calculations, the results were translated into the following plots, on Figures 4.5 and 4.6.For easeness of visualization and identification of the different curves, only a short sample of five minutesis presented in the figures related to speed, both here and in Section 4.3.

Figure 4.5: Speeds comparison including the dynamic prediction

Checking the difference between OBD speed and dynamic speed prediction every second, for the datapresented in Figure 4.5 the maximum difference between two curves is of 3,32 km/h, while the maximumvalue is 44 km/h for the OBD speed against a top dynamic speed of 44,04 km/h, which is a negligibledifference. For the complete trip, the maximum OBD speed is of 87 km/h, the dynamic speed predictionpeaks at 84,88 km/h, with a maximum difference of 8,82 km/h.

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Figure 4.6: Total distances comparison including the dynamic prediction

Following the legend in Figure 4.6, the total distance obtained from each set of data was as follows inTable 4.2:

Legend Distance [m]OBD Distance 19916,39GPS Distance 20184,78

Dynamic Distance prediction 20490,67

Table 4.2: Resulting distances for the trip, using the three methods

The comparison of the three results was used as a way to validate the method developed in this chapterfor the dynamic prediction. The analysis of the results is presented in Section 4.3.

4.3 Results Analysis

The two main concerns with the results were the zero speed situations and the negative values of speed.These two problems can be clearly seen in Figure 4.7, where the code was run without any constraintsapplied. Both situations were solved by forcing the dynamic speed prediction to result in a zero in thosesituations.

In the case of a null OBD speed it was considered that the dynamic speed should also be consideredas zero, since a speed equal to zero is only registered when the car is actually stopped. As seen inFigure 4.7, without this constraint the speed prediction has no zeros that last for more than a second,which would mean that the car never stops, a situation that actually happened multiple times during the

43

tests. The situation of negative results was easily solved by considering that these values should bezeros as well.

Figure 4.7: First model of the dynamic speed prediction, without any applied constraints

Looking at Figure 4.5 it is possible to see that the correction made to the values of speed with theacceleration creates a generally smoother curve. The situations where this is better noticed are peakson the OBD speed and sets of a few seconds where the speed originally collected varies, with lots ofups and downs in a short span. These would represent situations that are abnormal since these wouldrepresent a very fast increase and decrease of speed. This situation can probably be explained by anOBD sampling rate of 1 Hz mixed up with the low resolution of 1 km/h.

Concerning the calculations for distance, the expected result was that by using the dynamic speed pre-diction and performing an integration the calculated distance would be between the distances obtainedfrom both the OBD speed and from the GPS coordinates. As seen in Figure 4.6, the result does notconfirm the expectation. One can see that for the first part of the trip the behaviour is close to the predic-tion, but somewhere between the 400th and the 500th seconds the lines of GPS distance and Dynamicdistance prediction cross with each other.

A possible explanation for this result is that the coordinates collected with the GPS do not take into ac-count the travelled route between two points. This would mean that, theoretically, if two GPS coordinateswere separated by a section with an elevation in the route between them, when applying Equation 3.2the result would only be the shortest straight line between the two points and not the real distancebetween them.

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Another possible explanation can be that the results of both the OBD and the dynamic prediction (sinceit is based on the OBD data) are overestimated. This can be caused by the fact that even with thementioned correction applied to the speedometer speed when obtaining the OBD speed, this does notmean that the resulting speed is the real one. In that case the speedometer still has influence over thefinal results and even if all the corrections applied during the processing of data were very precise, therewould still be no guarantees that the tire radius was in accordance with the average tire wear consideredfor the speed indicated in the speedometer.

4.4 Conclusions

In this chapter the idea of testing a method that improves the results obtained directly from the informa-tion collected during the trips reveals itself as a viable option. Although the results do not agree entirelywith the initial predictions, the results are considered plausible and not at all unexplainable.

In the case of speed, the dynamic prediction works well and provides good results, leaving only somequestions regarding points with a dynamic prediction much lower than the OBD speed. The primary goalof improving the smoothness and resolution of speed was achieved but improving the dynamic speedprediction using information about the road slope is probably a good hypothesis to test in the future. Itsimplementation was put aside for this work because the sampling rate of altitude was not constant andin some cases the very fast detected variation of altitude led to very pronounced slopes.

It is also possible to see that the GPS equipment that is being used should be used to calculate speedonly as a last resort option. Looking at the red line in Figure 4.7 areas with unreliable results are visible.Possible causes for this are situations of multiple changes in speed and direction, crossing areas withpoor GPS signal and vibration affecting the GPS antenna, for example when riding on cobblestonepavement (figure available in the Appendix).

In the case of distance, the results are considered acceptable since all the methods achieve similarresults and there is no exact solution to establish a better comparison. However, potential technicalissues mentioned in Section 4.2 shall not be forgotten.

Further improvements would require a lot more testing and probably specific information on the vehiclewhere the OBU is installed should be available, for example, the speedometer corrections being appliedby the manufacturer.

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Chapter 5

Comfort

In this chapter the evaluation of comfort is presented. The first part concerns the methodology and allthe calculations related to comfort, following the rules established by the ISO 2631-1 standard, includingthe criteria to classify the different comfort levels.

The second section of the chapter is where the results are presented. This includes an overview of thedata collected during a whole trip, followed by the results of the calculation methods applied to each axisand combined into magnitude.

The third section includes the analysis of the results, focusing on the values presented in Table 5.1 [33].

Magnitude (m/s2) Comfort Level≤0,315 Not uncomfortable

0,315 to 0,630 A little uncomfortable0,500 to 1,000 Fairly uncomfortable0,800 to 1,600 Uncomfortable1,125 to 2,500 Very uncomfortable≥2,000 Extremely uncomfortable

Table 5.1: ISO 2631-1 comfort guidelines

An important goal of this chapter is verifying the possibility of identifying a certain event just by lookingat the magnitude or if the acceleration profile is needed. As explained below, most cases require theuse of the acceleration profile unless information about the location or input by the user is available.

To identify the events that will be closely looked at, the best way is certainly looking at either one of thethree axis filtered data, since the magnitude values combine all the axis and make it difficult to identifyany situation, except in very specific situations. For example, a hard braking event is predictably easilynoticeable in the magnitude profile, as it generates a value way above the others. Of course, looking atthe filtered signal only it is easier due to the use of positive and negative values, as in Figure 5.1.

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Figure 5.1: Filtered longitudinal acceleration, with indication on hard braking events

For vertical events of high intensity, such as crossing a speed bump at high speed or a long stretch ofcobblestones it is also easy to see the events in the vertical acceleration graph.

Figure 5.2: Filtered vertical acceleration, with indication on high speed crossing of speed bumps andcrossing of a cobblestone section

The speed bumps when crossed at high speed induce a high vertical acceleration, while the cobblestonepavement generates a behaviour very similar to a normal tarmac road in terms of frequency but withmuch higher values of acceleration.

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5.1 Methodology

As already mentioned in Chapter 1, the international standard ISO 2631-1 establishes reference valuesof acceleration to classify comfort in different levels.

The method used in this work is called the “basic evaluation method”, which requires the use of theweighted root mean square acceleration, as in Equation 5.1:

aw =

[1

T

T∫0

a2w(t)dt

] 12

(5.1)

Where T represents the time period between every two measured points which in this work is onesecond. As the goal was to classify the driving condition every second, to achieve it Equation 5.1 wasapplied to a set of every one hundred points, resulting in a single acceleration value for that data set andeffectively reducing the sampling rate to 1 Hz.

The second step, that allows for the effective use of the values of acceleration to evaluate comfort wasthe calculation of the magnitude, using Equation 5.2:

av = (a2x + a2y + a2z)12 (5.2)

Equation 5.2 effectively combines the three considered axis into one value, enabling the evaluation ofsituations where it is unclear if the acceleration in one direction is predominant over the others.

5.2 Results

Using the filtered axial accelerations obtained in Section 3.2.3, the root mean square values for eachaxis were calculated, using Equation 5.1. Figure 5.3 is an example of the comparison between thefiltered and RMS longitudinal accelerations. The figures for the vertical and lateral axes are available inthe Appendix.

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Figure 5.3: Longitudinal acceleration: filtered data (blue) and RMS value (black)

Analysing the three different RMS axial accelerations by computing the mean RMS acceleration, thelongitudinal axis presents the higher values while the vertical axis has the lowest mean value. This isprobably due to the fact that most of the driver inputs affect either the longitudinal or lateral behaviourof the car, while the vertical inputs are caused by outside interference and less common in an urbanenvironment. Table 5.2 shows the mean value for each axis during the itinerary:

Axis Mean acceleration [m/s2]Longitudinal 0,6246

Lateral 0,5754Vertical 0,3578

Table 5.2: Mean value of the RMS of each axis during the trip

The next and final step, is to apply Equation 5.2 to obtain the magnitude for every second. This is themeasure that allows to classify the different comfort levels according to the international standard.

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Figure 5.4: Resulting magnitude

Looking at Figure 5.4, it is possible to notice some events that generate higher acceleration values butit is impossible to tell them apart in general. This leads to the conclusion that magnitude is a simpleand fast method to evaluate comfort but further analysis of each axis root mean square individually isrequired in order to narrow the list of probable causes for magnitude variations.

5.3 Results Analysis

The first goal of this chapter is to classify the comfort condition in every second of a trip, following theguidelines of ISO 2631-1. Figure 5.5 represents the magnitude for each second of the trial including thethreshold values used for comfort throughout this work.

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Figure 5.5: Resulting magnitude, with the upper limit reference line for each comfort level

Comfort condition Lower limit (m/s2) Upper limit (m/s2)Not uncomfortable 0 ≤ av < 0,315 0 ≤ av < 0,315

A little uncomfortable 0,315 ≤ av < 0,500 0,315 ≤ av < 0,630Fairly uncomfortable 0,500 ≤ av < 0,800 0,630 ≤ av < 1,000

Uncomfortable 0,800 ≤ av < 1,125 1,000 ≤ av < 1,600Very uncomfortable 1,125 ≤ av < 2,000 1,600 ≤ av < 2,500

Extremely uncomfortable av ≥ 2,000 av ≥ 2,500

Table 5.3: Lower and upper limits from ISO 2631-1

The values used as reference were the upper limits of the international standard. This decision wasmade after a first more conservative approach, performing the evaluation with the lower limits, whichresulted in a trip that was much more uncomfortable in theory than in practice, an opinion confirmed bythe trials participants.

Comfort condition Time using lower limits [%] Time using upper limits [%]Not uncomfortable 4,08 4,08

A little uncomfortable 16,08 25,45Fairly uncomfortable 20,66 25,49

Uncomfortable 22,07 28,19Very uncomfortable 28,57 13,83

Extremely uncomfortable 8,54 2,96

Table 5.4: Comparison of the percentage of time spent in each comfort condition, using either the loweror upper limits of magnitude of vibration total values

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As the results of Table 5.4 show, there is a big difference between the two conditions. While using lowerlimits the total amount of time in at least an uncomfortable condition is almost 60%, using higher limitsputs that percentage at only 45%, which seems to be more realistic, but still quite high for a normal ride.

Even though the upper limits provide results more in accordance with the drivers and vehicle passengers’perception (information obtained during informal talks with the trial participants), there is still a feelingthat the values used in this guideline are too low. This may be caused by the fact that the guideline wascreated having in mind public transports, which obviously require tighter regulations.

Regarding specific situations that are considered as uncomfortable, those considered as more obviousare events with vertical acceleration inducers, such as cobblestones pavement, speed bumps or pot-holes. Speed bumps are widely known for being used to cause discomfort if crossed at high speeds.Other uncomfortable situations, related to longitudinal and lateral accelerations are more related withdriver behaviour or road conditions and usually are situations where comfort and safety evaluations willoverlap each other, e.g. hard braking.

The first situation presented is a stretch of cobblestone pavement with some very small and almostunnoticeable speed bumps, crossed at low speed, most of the time in the range of 20 to 30 km/h. Duringthe presented trial, this event happened during seconds 244 to 488.

Two assumptions are made when looking at a cobblestone section. The first is that the vertical accelera-tion has a clear predominance over the longitudinal and lateral components on influencing the resultingmagnitude. The second assumption that seems reasonable is considering that the longitudinal compo-nent represents the smaller part of the final result of magnitude. With these two assumptions in mind,the first analysis is done by comparing the total magnitude against the RMS of the vertical acceleration.

Figure 5.6: Magnitude and root mean square of the vertical acceleration in the cobblestone pavement

As predicted for this kind of pavement, during the analysis performed for this work the vertical acceler-

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ation has consistently reached higher values than the other axial components. The type of pavement,which is highly irregular, translates into an irregular acceleration behaviour. But contrary to expecta-tions, while the RMS of the vertical acceleration is the axial component with the higher value duringmost of time while crossing the cobblestone pavement section, the result is clearly far from providing afull explanation for the values of magnitude obtained. Given the circumstances, the RMS values of thelongitudinal and lateral accelerations are relatively stable throughout the crossing of the cobblestone butwith the lateral component being almost constantly higher.

Considering all the information available, it is considered reasonable to assume that a combination of theRMS of vertical and lateral accelerations will then provide an approximation very close to the vibrationtotal value (VTV).

Figure 5.7: Magnitude compared to the magnitude without including the root mean square of the longi-tudinal acceleration in the cobblestone pavement

The analysis of Figure 5.7 shows that most of the time the combination of the lateral and vertical ac-celeration represent an almost perfect approximation to the magnitude values and consequently, arethe two main contributors for the comfort classification while in a cobblestone pavement. The only mo-ment where there is a clear deviation is for about ten seconds, right before the mark of fifty seconds.This was caused for a moment of deceleration, caused almost certainly by braking when approaching apedestrian crossing.

Another important matter in this pavement seems to be the fact that the moment when a lower magnitudehappens corresponds to moments when the vehicle is moving slower, as seen comparing Figure 5.8.This relation seems to go in the same direction as the relation seen in speed bump events [19], hintingat the possibility of a relation between speed and comfort.

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Figure 5.8: Speed profile vs. magnitude, while crossing the cobblestone pavement section

The next event is the crossing of speed bumps. These are quite common features in urban roads aroundthe world, used in order to try that drivers refrain themselves from speeding by causing discomfort if ahigh speed is used. In order to evaluate the speed bumps effectiveness, the results include a speedbump crossed at almost 50 km/h and another one crossed at approximately 30 km/h, which was therecommended speed in the area.

Figure 5.9: Speed, longitudinal acceleration and vertical acceleration variation while approaching andcrossing a speed bump at approximately 30 km/h

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Figure 5.10: Magnitude and root mean square of the longitudinal and vertical accelerations, crossing aspeed bump at approximately 30 km/h

Looking at Figure 5.10, it is noticeable that the magnitude shows two different phases where highervalues are reached. These two peaks are easily justified by the two individual components shown in thesame figure. When crossing the speed bump, obviously represented by the second peak, the verticalcomponent of acceleration represents the higher influence in the magnitude value while the first peak iscaused by longitudinal acceleration. By using magnitude solely it is not possible to understand what kindof event caused the raise of the longitudinal acceleration, which leads to Figure 5.9. This figure showsthat the approach to the speed bump was done at a high speed and therefore the driver had to brake inorder to reduce to the desired speed of 30 km/h.

From the figure it is possible to see that while the vertical component is the main influence in the finalresult, the longitudinal component makes a difference, leading to a classification of ”uncomfortable” bya small margin.

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Figure 5.11: Magnitude and root mean square of the vertical acceleration, crossing a speed bump atapproximately 50 km/h

The crossing of a speed bump with the same characteristics but at a higher speed, close to 50 km/h, ledto a classification of ”very uncomfortable” by a significant margin. The speed of crossing represents anincrement of 66% compared to the recommended speed in the area.

Comparison between Figures 5.10 and 5.11, shows that there is a clear influence of the speed at whichthe vehicle is travelling when crossing the same kind of speed bump. It is clearly noticeable that crossingthe same type of speed bump at a speed about two thirds higher leads to a peak of vertical acceleration(2,212 m/s2) that is slightly more than double the vertical acceleration achieved at a lower speed (1,090m/s2), that complies with the legal speed limit of the location.

Comparing the results with the relation established by Jiang et al [19], presented in Table 5.5, wherethe comfort levels were also classified according to speed, the results fail to confirm the suggestedguidelines when considering situations where a speed bump is crossed at a higher speed.

If Table 5.5 was used as the sole reference, the speed bump from Figure 5.10 would be consideredas “fairly uncomfortable”, which is in accordance with the figure if only the vertical RMS accelerationis used but is a level short if the magnitude is considered. The same comparison for the speed bumpfrom Figure 5.11 would lead to a classification of “extremely uncomfortable”, but both magnitude andthe vertical RMS acceleration evaluate the speed bump only as “very uncomfortable”. So, establishinga comparison using the same criteria as Jiang et al [19] leads to the conclusion that the suggestedguidelines only achieve results in accordance with ISO 2631-1 when applied to low speed crossing ofspeed bumps.

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Speed range (km/h) Comfort Level≤24 Not uncomfortable

24 to 29 A little uncomfortable27 to 32 Fairly uncomfortable30 to 38 Uncomfortable35 to 43 Very uncomfortable≥40 Extremely uncomfortable

Table 5.5: Guidelines for comfort levels on speed bumps using speed as a reference

Out of the events that mainly generate acceleration in the longitudinal or lateral directions, hard brakingis the one that creates an overlap of comfort and safety. During the trial, under controlled conditions,a hard braking situation was performed while riding in a straight line, by braking until the ABS wasactivated. As this event was intended to be used to evaluate safety conditions, it is more focused inChapter 6, while here only the comfort conditions are shown.

Figure 5.12: Magnitude obtained while performing a hard braking event

Looking at Figure 5.4, the event presented in Figure 5.12 corresponds to the highest peak in the wholetrip, confirming the most logical prediction that an accident or pre-accident situation would be not only ahigh safety risk but a highly uncomfortable event.

While the three already mentioned situations were considered as uncomfortable by the vehicle passen-gers, other situations that qualify as ”extremely uncomfortable” according to the international standardwere not considered as such by the same passengers. A clear example of this is the sequence of aleft-right turn, in Figure 5.13.

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Figure 5.13: Comparison of the root mean square of the lateral acceleration and the magnitude whileperforming a left-right turn sequence

While the comfort classification could hypothetically be explained by the influence of longitudinal orvertical accelerations, this is not the case since the profiles in Figure 5.13 are closely related, with apeak value of 3,28 m/s2 for the lateral root mean square and 3,36m/s2 for the magnitude. A result likethis can have two possible explanations. One possibility is that the result backs up the idea that thescale is possibly flawed due to its use of a very tight scale, probably influenced by the need to adaptthe scale to public transports. Another possibility is the road design being unable to guarantee that avehicle going under the legal speed limits can stay inside the comfort or even the safety limits used asreference in this work, as seen in Figure 5.14. The event ranks as harsh, in terms of safety, as can beseen in Figure 5.14. This event is focused also in Chapter 6.

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Figure 5.14: Classification in terms of safety using lateral acceleration in the left-right turn sequence

5.4 Conclusions

In this chapter all the work developed regarding comfort was presented. The first part was more fo-cused in the methodology while the second and third parts included the obtained results and respectiveanalysis.

The method used for the evaluation of comfort was based on the ISO 2631-1 standard and the availablereferences. The process to characterise comfort can be summarized as:

• Filter data with a Butterworth filter

• Calculate the RMS of each axis, Equation 5.1

• Determine the magnitude or vibration total value (VTV), Equation 5.2

• Compare the magnitude values against the guidelines from Table 5.1 or other known applicableguidelines

Using the above list it is possible to obtain an evaluation of comfort conditions in a fast and reliable way.

Concerning the method used, the acceleration values were used by performing a single signal filteringin order to reduce noise and leaving out the use of frequency weightings. Although the influence of thefrequency weighting factors is probably not that strong in the final result and a similar method was usedby Jiang et al [19], it should be noted that slightly different results are possible.

A question arising from the evaluation of comfort is related to how restrictive the levels are. The goalof ISO 2631-1 in terms of comfort was to establish guidelines for public transports, that ended up beingconsidered by other authors for LDVs and other types of vehicles and with acceptable results.

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Considering situations where the magnitude levels are low, the evaluations are considered as accept-able. On the other hand, in some situations the reference values seem to be very low and are not inaccordance with the feelings of the few participants in the trials. This is noticeable by the 45% of timeconsidered as uncomfortable or worse and in lateral events classified as ”extremely uncomfortable”, bothsituations where the vehicle occupants did not feel as such.

As mentioned in the introduction to this chapter, one of the goals was understanding if just by lookingat the magnitude profile it is possible to identify a certain event. Looking at any of the events used asa case study, it becomes clear that none of the events’ causes are identifiable just by looking at themagnitude profile. Cases like the one presented in Figure 5.10, clearly show that the option of lookingonly at the magnitude profile is not a good method.

The option of looking solely at the axial RMS is also unreliable. For example, events with a high verticalacceleration can be anything among potholes, speed bumps or cobblestones. Very high longitudinalRMS is mostly associated with hard braking events but for most events it is impossible to establish adriver profile just by looking at it.

The only possible conclusion is that only the use of the raw or filtered acceleration data provide all theinformation needed to identify a certain event.

An interesting possibility in this chapter was to see how the relation established for comfort and speedby Jiang et al [19], using only the vertical acceleration instead of magnitude, fared when applied tospeed bump events data collected for this work. The results are not in complete accordance with thesuggested guidelines for comfort on speed bumps when using speed and vertical RMS acceleration ascriteria, as the results fail when evaluating events at higher speed. Since the suggested guidelines wereestablished by performing experiments in different types of speed bumps and possibly none of those wasof the same type as the ones used in this work, this probably explains the differences in the resultingclassifications, with the differences being noticed only in situations where higher speeds are used.

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Chapter 6

Safety

This chapter is focused more on classifying the safety conditions for the occupants and not so much inthe perception of those. This is easily explainable by the fact that not only the objective was never to putthe participants at risk and also by the fact that the experiments were performed on public roads and so,any activity that could pose any safety risk for the participants could possibly put the occupants of othervehicles at risk too.

Since for the evaluation of safety conditions there are no international standards or methods that areaccepted as being the best one, the analysis performed in this chapter is based only on values thatwere subject to a simple filtering and then compared to threshold values selected among those alreadyreferenced in Chapter 1. Below is presented a summary of the steps if the accelerometer is workingproperly:

• Filter data with a Butterworth filter

• Compare the longitudinal and lateral acceleration values against available guidelines

The main difference between safety and comfort, is that in the case of comfort conditions, while the threeaxis are considered, the vertical acceleration has the biggest influence in the result of situations that arecomfortable or uncomfortable, in the case of safety conditions the focus is only on the longitudinal andlateral accelerations. It must be noted that most situations that can be considered as inducing high levelsof discomfort due to longitudinal or lateral accelerations are usually also in a relatively low level of safety.

For the evaluation of safety, the derivatives of both GPS data and OBD data can be used, following therelations established in Figure ??. The reasons for the use of the derivatives is in the following list:

• For the evaluation of safety conditions the longitudinal and lateral accelerations are predominant

• There is a high probability of an overlap between unsafe and uncomfortable situations

• The possibility of the accelerometer being defective or the equipment falling off from its place andstaying in an incorrect position

The last item of course leads to unreliable data being collected and so the derivatives of distance andspeed are possible alternatives to minimize the problems created by that situation, since those are notdependent on the positioning of the OBU. The main drawback of using the data of distance and speedis that both of them are only representative of the longitudinal component of the vehicle movement andso the lateral acceleration is impossible to evaluate.

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Like what was done in Chapter 5, in this chapter specific events are used as examples with the resultsof using the different methods to obtain the longitudinal accelerations being compared to each other.

6.1 Methodology

The methodology for this section was much simpler than the one used in other chapters. The mainconcern to correctly evaluate the values of acceleration in this case was choosing an appropriate filter.Since for safety there is not a widely accepted filter as being the best, contrary to what happens in thecase of comfort, and in order to maintain the use of the OBU resources as low as possible, the samefilter that was used for comfort was used for safety. Of course, as mentioned in Section 3.2.3, the factthat the Butterworth filter did not eliminate the peaks allows for its use in this situation.

In terms of calculations, the main concern for this part was the already mentioned need to take intoaccount the need to keep tracking safety conditions if any problems related to the accelerometer arise.For this reason, the numerical differentiation methods from Section 3.2.2 were applied to both the OBDspeed and to the distance obtained from the GPS data.

Dhf(x1) =f(x1 + h)− f(x1 − h)

2h(6.1)

D2hf(x) =

f(x− h)− 2f(x) + f(x+ h)

h2] (6.2)

To establish the safety reference levels the option was to use the default values for longitudinal hard andextreme braking situations from Maxi Recorder User’s Manual [31]. The option for these values wasbased on the fact that without any international standard being enforced, then the best option would beto choose values being used by operating companies. Autel Company also establishes two differentlevels which is regarded as an important measure to differentiate, at 0,5g and 0,35g.

To define the lateral threshold values, for extreme lateral acceleration the relation referenced by Fe-lipe [18] for skidding was used, considering that an extreme lateral acceleration would be about 90% ofan extreme longitudinal acceleration and the value obtained this corresponds to approximately 0,45g.Applying the same difference of 0,15g, a value of 0,3g is selected to be used as reference for a not sosafe approach to a curve. The values being used are summarized in Table 6.1.

Safety Level Longitudinal deceleration (m/s2) Lateral acceleration (m/s2)Hard ≤ -3,43 ≤ |2,94|

Extreme ≤ -4,91 ≤ |4,42|

Table 6.1: Reference safety levels

6.2 Results

The results for this chapter are focused on the validation of the different methods used to evaluate theacceleration values, when applied using the chosen reference values. The first goal is to perform thisevaluation using the accelerometer data, that allows the testing of both longitudinal and lateral safety

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conditions. The second goal is to understand how accurate the alternative methods are when comparedto the previous evaluation, even with the associated limitations such as the lower sampling frequency orbeing limited to evaluate the longitudinal component.

Using the data available directly from the accelerometer, the resulting profiles for longitudinal and lateralwere obtained and can be seen in Figures 6.1 and 6.2. These represent the core of the safety evaluationprocess.

Figure 6.1: Longitudinal Butterworth filtered acceleration

Figure 6.2: Lateral Butterworth filtered acceleration

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Since the longitudinal acceleration is also looked upon for safety evaluation, the comparison betweenthe three different accelerations is of interest. The Butterworth filtered acceleration was reduced to a 1Hz sample in Figure in order to ease the comparison between the three images.

Figure 6.3: Comparison between the three different methods to obtain longitudinal acceleration, in a setof 500 seconds

The OBD speed derivative and collected acceleration profiles seem to have a very similar behaviour. Onthe other hand, the values of acceleration obtained from the GPS clearly show a very different behaviour,that seems to have generally much lower values compared to the other two methods for events in thesame moment.

6.3 Results Analysis

The first goal of the chapter is to obtain the amount of time spent under each safety condition during thetrip, in order to see how each method fares against the others.

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(a) Longitudinal acceleration (b) Lateral acceleration

Figure 6.4: Longitudinal and lateral acceleration components with the reference lines of each safetylevel

(a) OBD acceleration (b) GPS acceleration

Figure 6.5: Longitudinal acceleration components obtained from the OBD and GPS with the referencelines of each safety level

The reference values used are the ones presented in Table 6.1. Under those conditions, the percentageof time under each of them was determined.

Safety Level Longitudinal [%] Lateral [%] OBD [%] GPS [%]Normal 99,80 99,20 99,90 99,71Hard 0,10 0,77 0,10 0,25

Extreme 0,10 0,03 0,00 0,04

Table 6.2: Percentage of time in each level, using the different evaluation methods

Looking at the figures previously presented, something that is immediately noticeable is that the val-ues obtained from the GPS are not in line with the other longitudinal component values. Figures 6.4a

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and 6.5a clearly show that there was an event that led to a high deceleration around the 2300 seconds ofthe trip while the values obtained with the derivative of the GPS data have a peak of deceleration muchearlier, after approximately 2050 seconds, when no events were noticed. Even worse is the fact thatthe GPS shows a high acceleration with a peak of 10,07 m/s2 immediately before the high decelerationand looking at the graphic in Figure 6.5b it is possible to see that the real hard braking event performedduring the trip went unnoticed.

Comparing only the Figures 6.4a and 6.5a, the behaviour of both the profiles seems to be very similar,with the main difference being the apparent shrinkage suffered by the profile obtained from the OBDderivative, that leads to the braking event performed during the test not being considered as extremelyunsafe but only as a hard braking situation.

Concerning specific events, the two most interesting situations are turns with high values of lateralacceleration and braking events with highly negative deceleration.

The first situation being looked at is the sequence of turns already mentioned in Chapter 5.

Figure 6.6: Lateral acceleration (blue) and dynamic speed prediction (black) during left-right turn se-quence. The reference lines (cyan) represent the lateral acceleration safety levels.

This event was considered as extremely uncomfortable in Chapter 5 even if the occupants did not feel itas such. Since that was the case, it is possible to assume that the vehicle occupants never felt at riskwhile performing any of the two turns of this sequence and seems to be in accordance with Figure 6.6as it qualifies only as a hard turn but not extreme.

The sequence yields a maximum of 3,81 m/s2 when turning right and a maximum of 3,86 m/s2 when

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turning left. Combined with the information of speed this leads to the conclusion that probably the speedat which the turn is crossed is not the main cause of the induced lateral acceleration, at least whileperforming these large radius turns at relatively low speeds. There is a difference of about 6-7 km/hwhile performing the two curves, with the right turn being the one performed at a higher speed around42 km/h.

The next situation of interest is the passage through roundabout and the exit of it. This event is the onewith the highest lateral acceleration registered.

Figure 6.7: Lateral acceleration (blue) and dynamic speed prediction (black) during cross of a round-about. The reference lines (cyan) represent the lateral acceleration safety levels.

Figure 6.7 shows the whole process, including the moment of stoppage before entering the roundabout.While the turn performed to exit the roundabout is not a hard turn with a small radius, it has a slight slopedownwards and is slightly banked to the outside, inclining the vehicle towards its left. The main reasonfor the high acceleration value is probably due to the presence of a banked turn that in this specificsituation tends to send the vehicle towards the lower part of the road.

The speed at which this roundabout was crossed was always under the legal speed limit of 50 km/h,never even reaching 35 km/h, which clearly leaves open the possibility of the safety conditions beingjeopardized by poor road design.

The last situation being focused and relevant as a safety related event is the braking trying to simulatean accident or near accident situation. In this case, the longitudinal component of speed is the part ofinterest.

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Figure 6.8: Longitudinal acceleration during the braking event

As can be seen in Figure 6.8, the event is clearly considered as an extreme braking event. The maximumdeceleration is of -6,827 m/s2, a lot more than the reference limit of -4,91 m/s2.

Figure 6.9: Longitudinal acceleration during the braking event, obtained from the GPS coordinates

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Figure 6.10: Longitudinal acceleration during the braking event, obtained from the OBD data

The validity of both of the approximations seems to be dubious. In the case of acceleration obtainedfrom the GPS (Figure 6.9), the results are clearly unreliable as the acceleration profile barely crossesthe reference line for a hard braking event, with a minimum of -3,499 m/s2. The acceleration obtainedfrom the OBD (Figure 6.10) results in a condition of hard braking event, with a peak of deceleration of-4,444m/s2. While this result is far from the values detected by the accelerometer, it still allows for abetter notion of the driver behaviour.

6.4 Conclusions

This chapter includes all the work developed concerning safety. The objectives included evaluating howtrustful the reference values used are and see how each of the methods presented in the methodologyfare against each other, when applicable.

The first and biggest problem in this area is that there are no international standards defining what isand what is not acceptable in terms of acceleration values to characterize an event as safe or unsafe,hence why the decision was made to use values in use in the industry. While those values seem tobe reasonably in accordance with what the vehicle occupants felt during the trials, they still lack anexplanation of how and why they were obtained. For example, the values used by Geotab are the samefor longitudinal and lateral accelerations, which does not seem correct while Autel is only concernedwith the longitudinal acceleration, with slightly higher values compared to Geotab. The main reason forchoosing the values used by Autel were due to the use of three different safety risk levels instead ofonly two. Starting from there, to obtain the reference values for the lateral acceleration the equationreferenced by Felipe [18] for the risk of skidding was used, considering a limit of 90% of the limit forlongitudinal acceleration.

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Regarding events where the lateral acceleration is dominant, it must be pointed out that research isscarce. Nevertheless, the values used give a very high percentage of time during the trip where thelateral conditions were considered as being perfectly safe, with the 0,03% of time considered as unsafenot being in accordance with the participants’ feeling during the trials. While contradicting the perceptionof the occupants, it is acceptable for lateral safety conditions to be stricter, as it can lead to losing controlof the vehicle due to the nature of the event.

Other source of concern, even if the vehicle occupants perception is not taken into account, is the factthat as seen in Section 6.3, some areas generate a situation considered as extremely unsafe even whenthe vehicle is travelling well below the legal speed limit, leading to two possible conclusions: either thevalues being used are indeed too strict or there are effects created by a possibly deficient road designthat leads to unsafe situations.

Concerning the different evaluation methods, for events related to longitudinal acceleration, namelybraking, the results quality is varied. The collected acceleration, after filtering, seems to give results inconcordance with the feeling of the vehicle occupants. Acceleration obtained by differentiation of theOBD data is a passable alternative if the concern is only in trying to identify a qualitative pattern in thedriver behaviour, due to the failure in correctly quantifying high intensity events. The results obtainedby differentiation of the distance calculated using the GPS coordinates are completely unreliable, barelyconsidering an extreme braking event as a hard event. Difficulties when using the GPS data werealso noticed when trying to find the vehicle speed which could help predicting possible difficulties whenfinding acceleration.

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Chapter 7

Conclusions and Future Work

7.1 Conclusions

The first objective of this work was to ascertain the possibility of evaluating comfort and safety conditionsin a vehicle based on a dynamic characterisation using acceleration data. The review of previous worksshows that clearly this is not a new idea, with research on this area going on since the 1940s and witha clear evolution in the available equipment, that started as big installations prepared specifically forthis kind of work, moving to on-board systems that occupied almost the same room as a human andvehicle observations from the outside and finally in the 1990s the advent of small On-Board Units. Allof these different approaches showed that the idea of evaluating the driving profile based on dynamiccharacteristics is not out of sense.

The second objective was to define the metrics and the methods to perform the characterisation ofdriver behaviour using only dynamic variables. This is based on the availability of modern low-cost andtechnologically developed On-Board Units combined with the admitted interest of insurance companiesin developing a new business area by providing a new service that can lead to lower spending with smallinvestment.

The work was then divided in two parts: evaluation of comfort and evaluation of safety. Each part wastreated differently due to the specificities of each part and the need to use completely different methods,as explained in Chapters 5 and 6.

Concerning comfort, it was found that an international standard is in force and so its use was consideredas mandatory in terms of calculations and the suggested reference values for magnitude were used. Themain conclusion is that, in general, the classification attributed by the norm is trustful, especially whentalking about low values of magnitude. The main concern is with events considered as uncomfortable ofworse, as the difference of acceleration limits between different levels seems to be based on a very tightscale, even when using the more relaxed approach. The results obtained consider the trials as beinguncomfortable or worse in almost 45% of the time, not in accordance with the feeling of the participants.Especially when considering events that lead to higher lateral accelerations, it seems that the resultsare easily put in extreme levels. These very strict levels are certainly influenced by the development ofISO 2631-1 for public transportation, where high accelerations in any direction have much more effectthan on a car. Not only passengers can travel still standing, for example, but even if seated usually theseats are not as comfortable as what is found in a light-duty vehicle (LDV) nor the same instruments are

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available to keep the person in place, such as seat belts.

Concerning the directions of acceleration that influence the most the comfort condition, the vertical andlateral are the most relevant. The longitudinal component tends to represent an uncomfortable conditionunder hard braking or worse, when the safety conditions are already considered risky as well.

Regarding safety, there is no specified method defined by an international standard and the availableresearch provides very different methods and results from one author to another. For the safety evalua-tion the reference values used were then based on data already in use in the fleet management industryfor longitudinal values together with information referenced by [18] to define the lateral reference values.Three different methods were used to evaluate the longitudinal acceleration (accelerometer and differ-entiation of both GPS distance and OBD speed) with all of them resulting in a normal safety condition onmore than 99% of the time. The most straight forward method, using accelerometer data, was the onlyone that led to a significant percentage of time under extreme safety conditions while providing reliableresults. In the case of OBD data, the results are passable as the inability of the method to detect higheracceleration values makes it only viable as a last resort. In the case of the GPS data, the results arecompletely wrong when looking for extreme events and if the tested numerical methods are to be used,this option should not be considered unless the hardware quality improves in the future. Concerninglateral safety, as in comfort related situations, the participants did not feel unsafe while performing anyturn, contrary to the 0,03% of time indicated by the results. While contradicting the participants’ per-ception, a more conservative approach to lateral safety conditions is acceptable, as events that inducehigher lateral accelerations can easily result in losing control of the vehicle.

The dynamics evaluation can be considered as an additional component of the work, intending to obtaina more correct speed than the one obtained through the OBD and improve the resolution. The applica-tion of restrictions on the resulting dynamic speed prediction was needed to improve the results and themethod lacks the inclusion of slope effect in the results. In the case of distance, even though the resultsare not in accordance with the initial expectations, the results’ proximity is enough to consider that thedeveloped model is valid for application.

7.2 Future Work

Suggestions for further development of the work developed for this thesis are vast. Those could be:

• On comfort:

– Perform an analysis in the frequency domain;

– Develop acceleration threshold values specifically for LDVs;

• On safety:

– Further research to improve the data regarding safety reference values;

– Perform research concerning wet road conditions. As a first approximation, data from Fig-ure 7.1 can possibly be used;

• On dynamics:

– Include the effect of road slope, either by collecting data of the road slope itself or usingaltitude values to approximate the slope value. The methodology developed by Gao [39]

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should also be considered;

• Look for better correlation between safety, comfort and the vehicle occupants’ perception;

• Use a scale to quantify the injury level in case of accident or near-accident;

Further analysis will require the use of large samples of individuals to ensure statistical validity for theresults.

Figure 7.1: Coefficients of friction for different roadway surfaces, comparing dry and wet condition [46]

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Appendix

Additional Figures

GPS Speed Problems

As mentioned in Chapter 4.4, when travelling over a cobblestone road stretch the resulting GPS speedis highly unstable, possibly caused by vibration affecting the GPS antenna behaviour.

Figure 2: Speeds comparison during the cobblestone pavement crossing. Problems when using theGPS data are clear

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Filtered and RMS Accelerations Comparison

Figure 3: Lateral acceleration: filtered data (blue) and RMS value (black)

Figure 4: Vertical acceleration: filtered data (blue) and RMS value (black)

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Butterworth Filter

In order to filter the accelerometer data a Butterworth filter was chosen. This part of the Appendixpresents a short explanation on the theory behind the use of this filter and how it is implemented inMatlab.

The Butterworth filter, also called Maximally Flat Response filter, is an Infinite Impulse Response filterthat has the flattest possible frequency response in the passband and the magnitude square for order nis given by:

|H(jω)|2 =1

1 + ( jωjωc

)2n(1)

Where n is the order of the filter and ωc is the cutoff frequency.

Figure 5: Butterworth filter with different n orders compared to the ideal response filter

The filter transfer function is obtained from:

H(s)H(−s) =1

1 + ( sjωc

)2n(2)

And poles of H(s)H(−s) are at:

s = (−1)12n jωc (3)

sk = (e)j(2k+n−1)π

2n (4)

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The poles are then represented in the unity circle. To establish a stable transfer function, the poles ofH(s) in the left-hand plan must be selected, resulting in:

H(s) =1∏n

k=1(s− sk)(5)

For the code development in Matlab, the Butterworth filter was designed using the “butter” function,which uses an analog prototyping method. selecting the order, normalized cutoff frequency between 0and 1 (Wn in Matlab environment) and bandpass type of filter in order to define the transfer functioncoefficients, b and a.

The coefficients are applied in the following transfer function:

H(s) =B(s)

A(s)=

b(1)sn + b(2)sn−1 + ...+ b(n+ 1)

a(1)sn + a(2)sn−1 + ...+ a(n+ 1)(6)

The normalized cutoff frequency is calculated as:

Wn =fc × 2

fs(7)

where fc is the desired cutoff frequency that defines the bandpass limit and fs is the sampling frequency.

This section was based on:

“Chapter 7. FIR and IIR Filters”, Notes on Discrete Signals and Systems, 2004, Sabanci University,Istanbul, Turkey;

“Butterworth Filters”, Lecture Notes for Signals and Systems, 2011, Massachusetts Institute of Technol-ogy, MA, USA through MIT OpenCourseWare.

”Butterworth filter design, butter function”, Mathworks Matlab 2014b documentation

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