According to NCHRP Report 493, there are five major left turn signal indications for permitted operations in the United States. They are: Circular Green (CG), Flashing Circular Red (FCR), Flashing Red Arrow (FRA), Flashing Circular Yellow (FCY) and Flashing Yellow Arrow (FYA). The main goal of this thesis is to study the driver behavior and analyze safety of drivers for different left turn indications using a real-time driving simulator. Different signal indications alter driver behavior which influences velocity and acceleration profiles. These profiles influence vehicular emissions and hence need to be studied as well. For this purpose, different scenarios are implemented in the driving simulator. Data is analyzed using Microsoft Excel, JMP Statistical tool and MATLAB. Safety of drivers is analyzed with respect to the parameter "Time to Collision (TTC)" which is directly obtained from simulator data. Vehicular emissions and fuel consumption are calculated using VT-Micro microscopic emissions model. Graphs are plotted for TTC and total emissions. Results indicate that for a day-time scenario, FCY and FYA are the most suitable left-turning indications whereas FCR and FRA are most suitable for a night-time scenario. / Master of Science

In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, Driver Assistance Systems (DAS) such as cruise control, Anti-Lock Braking Systems (ABS), and Electronic Stability Control (ESC) have become widely popular in the automotive industry. Therefore, vehicle control research attracts attention from both academia and industry, and has been an active area of vehicle research for over 30 years, resulting in impressive DAS contributions. Although current vehicle control systems have improved vehicle safety and performance, there is room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming local driving environment such as terrain roughness, elevation grade, bank angle, curvature, and friction. The local driving environment is measured in advance with a terrain measurement system to provide terrain data. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing the response measurements of a preceding vehicle. The response measurements of a preceding vehicle are acquired through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. The identification method analyzes the response measurements of a preceding vehicle to estimate road data. The estimated road data or the pre-measured road data is used as the upcoming driving environment information for the developed vehicle control system. The metric that objectively quantifies vehicle performance, the Performance Margin, is developed to accomplish the control objectives in an efficient manner. The metric is used as a control reference input and continuously estimated to predict current and future vehicle performance. Next, the predictive control algorithm is developed based on the upcoming driving environment and the performance metric. The developed system predicts future vehicle dynamics states using the upcoming driving environment and the Performance Margin. If the algorithm detects the risks of future vehicle dynamics, the control system intervenes between the driver's input commands based on estimated future vehicle states. The developed control system maintains vehicle handling capabilities based on the results of the prediction by regulating the metric into an acceptable range. By these processes, the developed control system ensures that the vehicle maintains stability consistently, and improves vehicle performance for the near future even if there are undesirable and unexpected driving circumstances. To implement and evaluate the integrated systems of this work, the real-time driving simulator, which uses precise real-world driving environment data, has been developed for advanced high computational vehicle control systems. The developed vehicle control system is implemented in the driving simulator, and the results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS. / Ph. D. / In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, cruise control, Anti-Lock Braking Systems, and Electronic Stability Controls have become widely popular in the automotive industry. Although vehicle control systems have improved vehicle safety and performance, there is still room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming driving conditions such as road roughness, elevation grade, bank angle, and curvature. The driving environment is measured in advance with a terrain measurement system. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing a preceding vehicle’s response to the road. The combined road data is used as the upcoming driving environment information. The measurement that indicates vehicle performance, the Performance Margin, is developed to accomplish the research objectives. It is used in the developed control system, which predicts future vehicle performance. If the system detects future risks, the control system will intervene to correct the driver’s input commands. By these processes, the developed system ensures that the vehicle maintains stability, and improves vehicle performance regardless of the upcoming and unexpected driving conditions. To implement and evaluate the proposed systems, a driving simulator has been developed. The results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

The Type-II dilemma zone refers to the segment of roadway approaching an intersection where drivers have difficulty deciding to stop or proceed through at the onset of the circular yellow (CY) indication. Signalized intersection safety can be improved when the dilemma zone is correctly identified and steps are taken to reduce the likelihood that vehicles are caught in it. This research employs driving simulation as a means to collect driver response data at the onset of the CY indication to better understand and describe the dilemma zone. The data obtained was compared against that from previous experiments documented in the literature and the evidence suggests that driving simulator data is valid for describing driver behavior under the given conditions. Fuzzy logic was proposed as a tool to model driver behavior in the dilemma zone, and three such models were developed to describe driver behavior as it relates to the speed and position of the vehicle. These models were shown to be consistent with previous research on this subject and were able to predict driver behavior with up to 90% accuracy. / Graduation date: 2013

The main objective of this research is the development of a framework for the automatic generation of systems of kinematic and dynamic equations that are suitable for real-time applications. In particular, the efficient simulation of constrained multibody systems is addressed. When modelled with ideal joints, many mechanical systems of practical interest contain closed kinematic chains, or kinematic loops, and are most conveniently modelled using a set of generalized coordinates of cardinality exceeding the degrees-of-freedom of the system. Dependent generalized coordinates add nonlinear algebraic constraint equations to the ordinary differential equations of motion, thereby producing a set of differential-algebraic equations that may be difficult to solve in an efficient yet precise manner. Several methods have been proposed for simulating such systems in real time, including index reduction, model simplification, and constraint stabilization techniques. In this work, the equations of motion are formulated symbolically using linear graph theory. The embedding technique is applied to eliminate the Lagrange multipliers from the dynamic equations and obtain one ordinary differential equation for each independent acceleration. The theory of Gröbner bases is then used to triangularize the kinematic constraint equations, thereby producing recursively solvable systems for calculating the dependent generalized coordinates given values of the independent coordinates. For systems that can be fully triangularized, the kinematic constraints are always satisfied exactly and in a fixed amount of time. Where full triangularization is not possible, a block-triangular form can be obtained that still results in more efficient simulations than existing iterative and constraint stabilization techniques. The proposed approach is applied to the kinematic and dynamic simulation of several mechanical systems, including six-bar mechanisms, parallel robots, and two vehicle suspensions: a five-link and a double-wishbone. The efficient kinematic solution generated for the latter is used in the real-time simulation of a vehicle with double-wishbone suspensions on both axles, which is implemented in a hardware- and operator-in-the-loop driving simulator. The Gröbner basis approach is particularly suitable for situations requiring very efficient simulations of multibody systems whose parameters are constant, such as the plant models in model-predictive control strategies and the vehicle models in driving simulators.

Med fler än en miljon omkomna i trafikolyckor världen över varje år är trafiksäkerhet ett ständigt aktuellt område. Studier på deprimerade patienter har visat att negativ sinnesstämning medför försämrad körförmåga. Dessa effekter är i hög grad outforskade och det är därför viktigt att undersöka om de förekommer även vid en mildare grad av nedstämdhet, så kallad dysfori, vilket i så fall skulle innebära att negativ sinnesstämning i likhet med trötthet och alkoholpåverkan utgör en allvarlig trafikfara. För att bättre förstå hur sinnesstämning påverkar körförmåga är det dock också relevant att undersöka om dysfori kan bidra till riskkompensation, det fenomen som inträffar när människor kompenserar för säkerhetsförändringar genom ett förändrat riskbeteende. I denna uppsats beskrivs en del av en körsimulatorstudie kring dysfori och bilkörning, där syftet var att undersöka om dysfori kan vara en orsak till riskkompensation. 15 studenter vid Linköpings universitet delades upp i en testgrupp med dysforiska försöksdeltagare (N = 5) och en kontrollgrupp (N = 10) med hjälp av Major Depression Inventory, ett instrument för att diagnostisera depression. Dessa fick sedan genomföra en körning i simulatorn Desktop T&D där time headway, time to collision, genomsnittshastighet och antal omkörningar mättes för de olika grupperna för att undersöka förekomsten av riskkompensation. Resultatet visade att inga signifikanta skillnader kunde observeras för något av måtten. Riskkompensation har i många studier visat sig vara ett komplext fenomen att undersöka och ett flertal metodologiska problem förelåg, särskilt på grund av svårigheten att mäta risk i en simulator med god validitet. Det är dock viktigt att fortsatta undersökningar görs för att bättre förstå riskkompensation, samt att fenomenet beaktas som en tänkbar inverkande faktor i framtida studier av körförmåga.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Fahrerassistenzsysteme (FAS), wie zum Beispiel die „Aktive Gefahrenbremsung“, sollen dazu beitragen, das Fahren sicherer zu machen und die Anzahl an Unfällen und Verunglückten im Straßenverkehr weiter zu senken. Bei der Entwicklung von FAS muss neben der funktionalen Zuverlässigkeit des FAS sichergestellt werden, dass der Fahrer die Assistenzfunktion versteht und fehlerfrei benutzen kann. Zur Bestimmung geeigneter Systemauslegungen kommen in der Entwicklung Probandenversuche zum Einsatz, bei denen die zukünftigen Nutzer das FAS erleben und anschließend beurteilen. In dieser Arbeit wird die Eignung eines statischen Fahrsimulators für die Durchführung von Probandenversuchen zur Bewertung aktiv eingreifender FAS untersucht. Hierzu wurde ein Fahrversuch auf der Teststrecke und im statischen Fahrsimulator konzipiert, mit jeweils ca. 80 Probanden durchgeführt und die Ergebnisse bezüglich der Auswirkung des FAS „Aktive Gefahrenbremsung“ auf ausgewählte objektive und subjektive Kennwerte in der jeweiligen Versuchsumgebung vergleichend gegenübergestellt. Es zeigt sich, dass der statische Fahrsimulator prinzipiell für die Durchführung von Studien zur Bewertung aktiv eingreifender FAS geeignet ist. Als Ergebnis der Arbeit werden Erkenntnisse zur Aussagekraft der betrachteten Kennwerte sowie Empfehlungen zur Versuchsdurchführung im statischen Fahrsimulator gegeben.

The main objective of this research is the development of a framework for the automatic generation of systems of kinematic and dynamic equations that are suitable for real-time applications. In particular, the efficient simulation of constrained multibody systems is addressed. When modelled with ideal joints, many mechanical systems of practical interest contain closed kinematic chains, or kinematic loops, and are most conveniently modelled using a set of generalized coordinates of cardinality exceeding the degrees-of-freedom of the system. Dependent generalized coordinates add nonlinear algebraic constraint equations to the ordinary differential equations of motion, thereby producing a set of differential-algebraic equations that may be difficult to solve in an efficient yet precise manner. Several methods have been proposed for simulating such systems in real time, including index reduction, model simplification, and constraint stabilization techniques. In this work, the equations of motion are formulated symbolically using linear graph theory. The embedding technique is applied to eliminate the Lagrange multipliers from the dynamic equations and obtain one ordinary differential equation for each independent acceleration. The theory of Gröbner bases is then used to triangularize the kinematic constraint equations, thereby producing recursively solvable systems for calculating the dependent generalized coordinates given values of the independent coordinates. For systems that can be fully triangularized, the kinematic constraints are always satisfied exactly and in a fixed amount of time. Where full triangularization is not possible, a block-triangular form can be obtained that still results in more efficient simulations than existing iterative and constraint stabilization techniques. The proposed approach is applied to the kinematic and dynamic simulation of several mechanical systems, including six-bar mechanisms, parallel robots, and two vehicle suspensions: a five-link and a double-wishbone. The efficient kinematic solution generated for the latter is used in the real-time simulation of a vehicle with double-wishbone suspensions on both axles, which is implemented in a hardware- and operator-in-the-loop driving simulator. The Gröbner basis approach is particularly suitable for situations requiring very efficient simulations of multibody systems whose parameters are constant, such as the plant models in model-predictive control strategies and the vehicle models in driving simulators.

La réalité augmentée (Augmented Reality ou AR) peut potentiellement changer significativement l’expérience utilisateur. Au contraire les applications sur Smartphone ou tablette, les technologies d’affichage tête haute (Head Up Display ouHUD) aujourd’hui sont capables de projeter localement sur une zone du pare-brise ou globalement sur tout le pare-brise. Le conducteur peut alors percevoir l’information directement dans son champ de vision. Ce ne sont pas que les informations basiques comme vitesse ou navigation, le système peut aussi afficher des aides, des indicateurs qui guident l’attention du conducteur vers les dangers possibles. Il existe alors un chalenge scientifique qui est de concevoir des visualisations d’interactions qui s’adaptent en fonction de l’observation de la scène mais aussi en fonction de l’observation du conducteur. Dans le contexte des systèmes d’alerte de collision avec les piétons (Pedestrian Collision Warning System ou PCWS), l’efficacité de la détection du piéton a atteint un niveau élevé grâce à la technologie de vision. Pourtant, les systèmes d’alerte ne s’adaptent pas au conducteur et à la situation, ils deviennent alors une source de distraction et sont souvent négligés par le conducteur. Pour ces raisons, ce travail de thèse consiste à proposer un nouveau concept de PCWS avec l’AR (nommé the AR-PCW system). Premièrement, nous nous concentrons sur l’étude de la conscience de la situation (Situation Awareness ou SA) du conducteur lorsqu’il y a un piéton présent devant le véhicule. Nous proposons une approche expérimentale pour collecter les données qui représentent l’attention du conducteur vis-à-vis du piéton (Driver Awareness of Pedestrian ou DAP) et l’inattention du conducteur vis-à-vis de celui-ci (Driver Unawareness of Pedestrian ou DUP). Ensuite, les algorithmes basées sur les charactéristiques, les modèles d’apprentissage basés sur les modèles discriminants (ex, Support Vector Machine ou SVM) ou génératifs (Hidden Markov Model ou HMM) sont proposés pour estimer le DUP et le DAP. La décision de notre AR-PCW system est effectivement basée sur ce modèle. Deuxièmement, nous proposons les aides ARs pour améliorer le DAP après une étude de l’état de l’art sur les ARs dans le contexte de la conduite automobile. La boite englobante autour du piéton et le panneau d’alerte de danger sont utilisés. Finalement, nous étudions expérimentalement notre système AR-PCW en analysant les effets des aides AR sur le conducteur. Un simulateur de conduite est utilisé et la simulation d’une zone HUD dans la scène virtuelle sont proposés. Vingt-cinq conducteurs de 2 ans de permis de conduite ont participé à l’expérimentation. Les situations ambigües sont créées dans le scénario de conduite afin d’analyser le DAP. Le conducteur doit suivre un véhicule et les piétons apparaissent à différents moments. L’effet des aides AR sur le conducteur est analysé à travers ses performances à réaliser la tâche de poursuite et ses réactions qui engendrent le DAP. Les résultats objectifs et subjectifs montrent que les aides AR sont capables d’améliorer le DAP défini en trois niveaux : perception, vigilance et anticipation. Ce travail de thèse a été financé sur une bourse ministère et a été réalisé dans le cadre des projets FUI18 SERA et Labex MS2T qui sont financé par le Gouvernement Français, à travers le programme « Investissement pour l’avenir » géré par le ANR (Référence ANR-11-IDEX-0004-02). / Augmented reality (AR) can potentially change the driver’s user experience in significant ways. In contrast of the AR applications on smart phones or tablets, the Head-Up-Displays (HUD) technology based on a part or all wind-shield project information directly into the field of vision, so the driver does not have to look down at the instrument which maybe causes to the time-critical event misses. Until now, the HUD designers try to show not only basic information such as speed and navigation commands but also the aids and the annotations that help the driver to see potential dangers. However, what should be displayed and when it has to be displayed are still always the questions in critical driving context. In another context, the pedestrian safety becomes a serious society problem when half of traffic accidents around the world are among pedestrians and cyclists. Several advanced Pedestrian Collision Warning Systems (PCWS) have been proposed to detect pedestrians using the on-board sensors and to inform the driver of their presences. However, most of these systems do not adapt to the driver’s state and can become extremely distracting and annoying when they detect pedestrian. For those reasons, this thesis focuses on proposing a new concept for the PCWS using AR (so called the AR-PCW system). Firstly, for the «When» question, the display decision has to take into account the driver’s states and the critical situations. Therefore, we investigate the modelisation of the driver’s awareness of a pedestrian (DAP) and the driver’s unawareness of a pedestrian (DUP). In order to do that, an experimental approach is proposed to observe and to collect the driving data that present the DAP and the DUP. Then, the feature-based algorithms, the data-driven models based on the discriminative models (e.g. Support Vector Machine) or the generative models (e.g. Hidden Markov Model) are proposed to recognize the DAP and the DUP. Secondly, for the «What» question, our proposition is inspired by the state-of-the-art on the AR in the driving context. The dynamic bounding-box surrounding the pedestrian and the static danger panel are used as the visual aids. Finally, in this thesis, we study experimentally the benefits and the costs of the proposed AR-PCW system and the effects of the aids on the driver. A fixed-based driving simulator is used. A limited display zone on screen is proposed to simulate the HUD. Twenty five healthy middle-aged licensed drivers in ambiguous driving scenarios are explored. Indeed, the heading-car following is used as the main driving task whereas twenty three pedestrians appear in the circuit at different moment and with different behaviors. The car-follow task performance and the awareness of pedestrian are then accessed through the driver actions. The objective results as well as the subjective results show that the visual aids can enhance the driver’s awareness of a pedestrian which is defined with three levels: perception, vigilance and anticipation. This work has been funded by a Ministry scholarship and was carried out in the framework of the FUI18 SERA project, and the Labex MS2T which is funded by the French Government, through the program ”Investments for the future” managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02).