Au cours des dernières décennies, les constructeurs automobiles ont constamment introduit des innovations technologiques visant à rendre les véhicules plus sûrs. Le niveau de sophistication de ces systèmes avancés d’aide à la conduite s’est accru parallèlement aux progrès de la technologie des capteurs et de la puissance informatique intégrée. Plus récemment, une grande partie de la recherche effectuée par l'industrie et les institutions s'est concentrée sur l'obtention d'une conduite entièrement automatisée. Les avantages sociétaux potentiels de cette technologie sont nombreux, notamment des routes plus sûres, des flux de trafic améliorés et une mobilité accrue pour les personnes âgées et les handicapés. Toutefois, avant que les véhicules autonomes puissent être commercialisés, ils doivent pouvoir partager la route en toute sécurité avec d’autres véhicules conduits par des conducteurs humains. En d'autres termes, ils doivent pouvoir déduire l'état et les intentions du trafic environnant à partir des données brutes fournies par divers capteurs embarqués, et les utiliser afin de pouvoir prendre les bonnes décisions de conduite sécurisée. Malgré la complexité apparente de cette tâche, les conducteurs humains ont la capacité de prédire correctement l’évolution du trafic environnant dans la plupart des situations. Cette capacité de prédiction est rendu plus simple grâce aux règles imposées par le code de la route qui limitent le nombre d’hypothèses; elle repose aussi sur l’expérience du conducteur en matière d’évaluation et de réduction du risque. L'absence de cette capacité à comprendre naturellement une scène de trafic constitue peut-être, le principal défi qui freine le déploiement à grande échelle de véhicules véritablement autonomes sur les routes.Dans cette thèse, nous abordons les problèmes de modélisation du comportement du conducteur, d'inférence sur le comportement des autres véhicules, et de la prise de décision pour la navigation sûre. En premier lieu, nous modélisons automatiquement le comportement d'un conducteur générique à partir de données de conduite démontrées, évitant ainsi le réglage manuel traditionnel des paramètres du modèle. Ce modèle codant les préférences d’un conducteur par rapport au réseau routier (par exemple, voie ou vitesse préférées) et aux autres usagers de la route (par exemple, distance préférée au véhicule de devant). Deuxièmement, nous décrivons une méthode qui utilise le modèle appris pour prédire la séquence des actions à long terme de tout conducteur dans une scène de trafic. Cette méthode de prédiction suppose que tous les acteurs du trafic se comportent de manière aversive au risque, et donc ne peut pas prévoir les manœuvres dangereux ou les accidents. Pour pouvoir traiter de tels cas, nous proposons un modèle probabiliste plus sophistiqué, qui estime l'état et les intentions du trafic environnant en combinant la prédiction basée sur le modèle avec les preuves dynamiques fournies par les capteurs. Le modèle proposé imite ainsi en quelque sorte le processus de raisonnement des humains. Nous humains, savons ce qu’un véhicule est susceptible de faire compte tenu de la situation (ceci est donné par le modèle), mais nous surveillerons sa dynamique pour en détecter les écarts par rapport au comportement attendu. En pratique, la combinaison de ces deux sources d’informations se traduit par une robustesse accrue des estimations de l’intention par rapport aux approches reposant uniquement sur des preuves dynamiques. En dernière partie, les deux modèles présentés (comportemental et prédictif) sont intégrés dans le cadre d´une approche décisionnel probabiliste. Les méthodes proposées se sont vues évalués avec des données réelles collectées avec un véhicule instrumenté, attestant de leur efficacité dans le cadre de la conduite autonome sur autoroute. Bien que centré sur les autoroutes, ce travail pourrait être facilement adapté pour gérer des scénarios de trafic alternatifs. / During the past few decades automakers have consistently introduced technological innovations aimed to make road vehicles safer. The level of sophistication of these advanced driver assistance systems has increased parallel to developments in sensor technology and embedded computing power. More recently, a lot of the research made both by industry and institutions has concentrated on achieving fully automated driving. The potential societal benefits of this technology are numerous, including safer roads, improved traffic flows, increased mobility for the elderly and the disabled, and optimized human productivity. However, before autonomous vehicles can be commercialized they should be able to safely share the road with human drivers. In other words, they should be capable of inferring the state and intentions of surrounding traffic from the raw data provided by a variety of onboard sensors, and to use this information to make safe navigation decisions. Moreover, in order to truly navigate safely they should also consider potential obstacles not observed by the sensors (such as occluded vehicles or pedestrians). Despite the apparent complexity of the task, humans are extremely good at predicting the development of traffic situations. After all, the actions of any traffic participant are constrained by the road network, by the traffic rules, and by a risk-aversive common sense. The lack of this ability to naturally understand a traffic scene constitutes perhaps the major challenge holding back the large-scale deployment of truly autonomous vehicles in the roads.In this thesis, we address the full pipeline from driver behavior modeling and inference to decision-making for navigation. In the first place, we model the behavior of a generic driver automatically from demonstrated driving data, avoiding thus the traditional hand-tuning of the model parameters. This model encodes the preferences of a driver with respect to the road network (e.g. preferred lane or speed) and also with respect to other road users (e.g. preferred distance to the leading vehicle). Secondly, we describe a method that exploits the learned model to predict the future sequence of actions of any driver in a traffic scene up to the distant future. This model-based prediction method assumes that all traffic participants behave in a risk-aware manner and can therefore fail to predict dangerous maneuvers or accidents. To be able to handle such cases, we propose a more sophisticated probabilistic model that estimates the state and intentions of surrounding traffic by combining the model-based prediction with the dynamic evidence provided by the sensors. In a way, the proposed model mimics the reasoning process of human drivers: we know what a given vehicle is likely to do given the situation (this is given by the model), but we closely monitor its dynamics to detect deviations from the expected behavior. In practice, combining both sources of information results in an increased robustness of the intention estimates in comparison with approaches relying only on dynamic evidence. Finally, the learned driver behavioral model and the prediction model are integrated within a probabilistic decision-making framework. The proposed methods are validated with real-world data collected with an instrumented vehicle. Although focused on highway environments, this work could be easily adapted to handle alternative traffic scenarios.

Traffic signals are very cost effective tools for urban traffic management in urban areas. The number of intersections in Sweden controlled by traffic signals has increased since the seventies, but efforts to study the traffic performance of the employed strategies are still lacking. The LHOVRA technique is the predominant isolated traffic signal control strategy in Sweden. Past-end green was originally incorporated as part of LHOVRA (the “O” function) and was intended to reduce the number of vehicles in the dilemma zone. Coordinated signal control in Sweden is often fixed-time with local vehicle actuated signal timing adjustments and bus priority. This research study was undertaken to increase the knowledge of the traffic performance impacts of these strategies. The aim was to evaluate the following control strategies using Stockholm as a case study: 1. The LHOVRA technique with a focus on the “O” function; 2. Fixed time coordination (FTC); 3. Fixed time coordination with local signal timing adjustment (FTC-LTA); 4. FTC-LTA as above + active bus priority (PRIBUSS); 5. Self-optimizing control (SPOT). Field measurements were used for study of driver behavior and traffic impacts as well as for collecting input data needs for simulation. The results from low speed approaches showed a higher proportion of stopped vehicles after receiving green extension. Moving the detectors closer to the stop line, and/or making the detectors speed dependent were suggested as measures to solve these problems. The VISSIM simulation model calibrated and validated with empirical data was used to study traffic performance and safety impacts of the LHOVRA technique as well as to test the suggested improvements. The simulation experiment results from these design changes were shown to reduce accident risk with little or no loss of traffic performance. TRANSYT was used to produce optimized fixed signal timings for coordinated intersections. HUTSIM simulations showed that local signal timing adjustment by means of past-end green was beneficial when applied to coordinated traffic signal control in the study area. Both delays and stops were reduced, although not for the main, critical intersection which operated close to capacity. To study the impacts of strategies for coordinated signal control with bus priority, extensive field data collection was undertaken during separate time periods with these strategies in the same area using mobile and stationary techniques. A method to calculate the approach delay was developed based on the observed number of queuing vehicles at the start and end of green. Compared to FTC-LTA, the study showed that PRIBUSS reduced bus travel time. SPOT reduced both bus and vehicle travel time. Future research efforts for the development of signal control strategies and their implementation in Sweden should be focused on strategies with self-optimization functionality. / QC 20100408

Driving under distraction may lead drivers to wrong actions that can result in serious accidents. The objective of this thesis was to apply a driving simulator to verify variations in drivers\' behavior while driving. Behavior to drive on a curve was measured by variation in drivers\' speed profile in a virtualized highway. The comparison was performed between two identical simulations, one involving drivers distracted with a mental workload, and other in which they were full aware of driving task. 54 volunteer drivers took part in this study, which was divided into 4 stages. 17 drivers performed the distraction test known as PASAT, and results showed that distracted drivers did not recognize the beginning of the curve and drove through it at speeds higher than those when they were fully aware. Moreover, driving performance was increased when drivers were aware of driving, thereby hitting high speeds in tangents, but perceiving curves in advance to reduce acceleration. This study confirms that driving simulators are beneficial in discovering drivers\' behavior exposed to activities that could be highly risky if driving in real situations. / A distração durante a atividade de direção pode levar o condutor de veículos automotores a cometer falhas, que podem ocasionar até mesmo acidentes graves. Este estudo aborda a utilização de simuladores de direção para verificar variações no comportamento de motoristas ao realizar a atividade de direção, distraídos ou com plena atenção na condução do veículo. O comportamento é medido pela variação no perfil de velocidade dos condutores para desenvolver uma curva considerada perigosa em uma rodovia simulada em ambiente virtual. A variação de velocidade deste perfil é comparada entre duas simulações idênticas, onde em uma delas os condutores estão distraídos com um teste que proporciona estresse mental e, na outra, estão com plena atenção à direção. 54 condutores fizeram parte deste estudo dividido em 3 etapas. 17 participantes realizaram o teste de distração conhecido como PASAT, e a análise dos resultados mostram que, distraídos, os condutores não perceberam o início da curva e desenvolveram velocidades maiores durante seu trajeto. Além disso, quando estavam com plena atenção à atividade de direção, o desempenho dos condutores foi melhor, atingindo velocidades maiores nas tangentes, mas percebendo as curvas antecipadamente e reduzindo suas velocidades antes de iniciar esses trechos.

In this thesis report we conducted research study on driver's behavior in T-Intersections using simulated environment. This report describes and discusses correlation analysis of driver's personality traits and style while driving at T-Intersections. The experiments were performed on multi user driving simulator under controlled settings, at Linköping University. A total of forty-eight people participated in the study and were divided into groups of four, all driving in the same simulated world. During the experiments participants were asked to fill a series of well-known self-report questionnaires. We evaluated questionnaires to get the insight in driver's personality traits and driving style. The self-report questionnaires consist of Schwartz's configural model of 10 values types and NEO-five factor inventory. Also driver's behavior was studied with the help of questionnaires based on driver's behavior, style, conflict avoidance, time horizon and tolerance of uncertainty. Then these 10 Schwartz's values are correlated with the other questionnaires to give the detail insight of the driving habits and personality traits of the drivers.

Static speed limits are the norm across the world’s roadway networks. However, advances in technology and increased applications in intelligent transportation systems (ITS) provide a mechanism for upgrading traditional speed limits into an active traffic management system. More specifically, variable speed limits (VSLs) can be used in high crash severity locations and in real-time congestion and weather events to increase traffic safety and operations. Much of the available literature on VSLs focuses upon crash prediction algorithms for VSLs, simulations, and effectiveness of real-world VSL implementations. One noticeable gap in the existing literature is related to driver compliance under varied configurations of alerting drivers of the variable speeds. An additional gap in literature is related to existence of a conceptual framework for identifying optimal corridors for potential VSL implementation. Within this thesis drivers’ willingness to comply with VSLs was investigated via focus groups and static surveys during the experimental process. Connections are made between driver speed choice and type of speed limit condition including uniform speed vi limit (USL) versus VSL, overhead mount versus side mount, presence of an explanatory message, and the numerical speed limit value. An analysis of the survey results was completed to isolate critical factors in VSL compliance. Opinions and perspectives on VSLs are derived through the focus group sessions Lastly, a case study approach is presented in which a region is chosen, and implementation metrics are analyzed on the major roadway networks using a GIS platform to create a composite ranking system for potential optimal VSL corridors. The study aims to be used as a foundation to justify use of certain types of VSLs in addition to creating a conceptual framework for VSL implementation zone identification.

Since their introduction to the 2009 Edition of the Manual on Uniform Traffic Control Devices, flashing yellow arrows (FYA) have had significant success in communicating the permissive turn message. While widely used for the permissive left turn maneuver, agencies recently have been utilizing flashing yellow arrows for the use with right turn applications as drivers interact with crossing pedestrians. As pedestrian conflicts are a concern during the permissive green phase, there is additional worry for the potential interaction between a pedestrian and vehicle turning right on red. This research explores the existing driver comprehension of permissive right turns during both green and red phases through static evaluation and microsimulation. Proposed traffic devices including the FYA and the Dynamic No Turn on Red sign were evaluated in relation to the existing signal and sign conditions implemented in the field. In comparing the proposed FYA to the existing circular green signal, the survey evaluation determined a statistically significant increase in drivers’ yielding responses when interacting with the FYA as opposed to the circular green. Through application of the VISSIM program, it was determined that right turning speeds with the FYA present were significantly lower than when interacting with solely the circular green. Both the static evaluation and microsimulation determined a strong similarity between the existing circular red and R10-11 sign and the proposed dynamic no turn on red sign which verifies the strong understanding drivers have of the message and the sign itself.

The objective of the current study is to evaluate the use of virtual reality (VR) headsets to measure driving performance. This is desirable because they are several orders of magnitude less expensive and, if validated, could greatly extend the powers of simulation. Out of several possible measures of performance that could be considered for evaluating VR headsets, the current study specifically examines drivers’ latent hazard anticipation behavior both because it has been linked to crashes and because it has been shown to be significantly poorer in young drivers compared to their experienced counterparts in traditional driving simulators and in open road studies. The total time middle-aged drivers spend glancing at a latent hazard and the average duration of each glance was also compared to these same times for younger drivers using a VR headset and fixed-based driving simulator. In a between-subject design, forty-eight participants were equally and randomly assigned to one out of four experimental conditions – two young driver cohorts (18 – 21 years) and two middle-aged driver cohorts (30 – 55 years) navigating either a fixed-based driving simulator or a VR-headset-based simulator. All participants navigated six unique scenarios while their eyes were continually tracked. The proportion of latent hazards anticipated by participants which constituted the primary dependent measure was found to be greater for middle-aged drivers than young drivers across both platforms. Results also indicate that the middle-aged participants glanced longer than their younger counterparts on both platforms at latent hazards, as measured by the total glance duration but had no difference when measured by the average glance duration. Moreover, the difference in the magnitude of performance between middle-aged and younger drivers was the same across the two platforms. There were also no significant differences found for the severity of simulator sickness symptoms across the two platforms. The study provides some justification for the use of virtual reality headsets as a way of understanding drivers’ hazard anticipation behavior.

The theoretical part of the diploma thesis focuses on the drivers reacting on a pedestrian crossing during the day and the night. A particular attention is paid to all the factors that can influence drivers' behaviour while driving through a pedestrian crossing. The influence of the behaviour by these factors often causes car accidents with pedestrians. One of the basic techniques is to analyze the behavior of the driver in the optical perception of stimuli that affect the optical response. Eyetracker is used to measure the optical responses. This device monitors changes of the angle of a driver's view, navigation of a driver and the driver's reaction on different stimuli while driving. The practical part of the thesis deals with an analysis of the drivers' behaviour while driving through a pedestrian crossing during the day and during the night. Twelve drivers took part in the day measurement of driving through the pedestrian crossing in Brno. Night measurement was taken place in Břeclav and Lednice with the attendance of seven drivers. During the ride are monitored the optical reactions of drivers on various impulses with the use of a special device Eyetracker.

The diploma thesis deals with the analysis of the influence of certain types of intersections and at the same time the time of day. The theoretical part describes the introduction to the factors affecting the driver when driving a motor vehicle. Furthermore, the work defines road junctions by law, types of road junctions and research on the issue of road junctions. The practical part of the diploma thesis is devoted to the evaluation of the total number of primary and secondary views of certain types of intersections and their average duration of day or night. At the same time, it evaluates the first direction of view according to the type of road (secondary road / main road) and the subsequent direction of travel. The obtained data are evaluated in the chapter discussion / analysis of results. The evaluated data in the first part showed that the secondary views have a shorter average duration compared to the average duration of the primary views. In the second part, the results of day and night were identical only at the roundabouts, when the drivers made the first direction of view to the left.

Abgelenktes Fahren gehört zu den Hauptursachen von Verkehrsunfällen und kann auf visuelle, manuelle oder kognitive Ablenkungsquellen zurückgeführt werden. Jede dieser Ablenkungsquellen wurde bereits mit negativen Effekten auf die Fahrerleistung in Zusammenhang gebracht. Obschon ein weitgehender Konsens über negative Auswirkungen von visueller/visuell-manueller Ablenkung besteht, sind die Wirkungen kognitiver Ablenkung auf Fahrfehler und Unfälle noch immer umstritten. Viele experimentelle Studien haben negative Auswirkungen kognitiver Ablenkung auf die Fahrerleistung berichtet. Demgegenüber stehen jedoch die Ergebnisse der Mehrzahl vorliegender „naturalistic driving studies“, die kein erhöhtes Unfallrisiko oder sogar protektive Effekte in diesem Zusammenhang fanden. Die aktuelle Entwicklung hin zu Mensch-Fahrzeug-Schnittstellen, die die Bedienung diverser Anwendungen mittels Sprachsteuerung ermöglichen, führt zu einem Anstieg von kognitiver Beanspruchung beim Fahren. Es ist daher von entscheidender Bedeutung, die Auswirkungen kognitiver Ablenkung auf die Fahrerleistung zu erfassen, um den Verantwortungsträgern in der Gesellschaft, den Regierungen und der Industrie eine Risikoabschätzung dieser Funktionen zu ermöglichen und die Sicherheit von Mensch-Fahrzeug-Schnittstellen zu erhöhen. Das Hauptziel dieser Dissertation bestand darin, die Effekte von kognitiver Ablenkung auf die Fahrerleistung zu untersuchen. Verschiedene Arten kognitiver Ablenkung, die sich beim Fahren unter realen Bedingungen häufig auf die Fahrer auswirken, wurden in dieser Arbeit kodiert und analysiert: kognitiv ablenkende Nebenaufgaben (z.B. telefonieren, singen), Fahreremotionen (z.B. Freude, Wut/Frustration, Traurigkeit) und Kombinationen von Fahreremotionen und Nebenaufgaben (z.B. Streit mit dem Beifahrer oder am Telefon). Bei der Untersuchung von Effekten kognitiver Ablenkung auf das Fahren sind Umwelt-, Situations- und Personenfaktoren zu berücksichtigen, da sie Mediator- und Moderatorvariablen bei der Erfassung des relativen Risikos von Ablenkung beim Fahren im Straßenverkehr darstellen. Daher folgte diese Dissertation dem ganzheitlichen Ansatz, so viele relevante Variablen wie möglich zu betrachten, die mit der Ausführung kognitiv ablenkender Tätigkeiten interagieren. Zu diesem Zweck wurden Daten der derzeit umfangreichsten „naturalistic driving study“ (the second Strategic Highway Research Program, SHRP 2) kodiert und analysiert, um möglichst viele Situationen, in denen eine kognitive Beanspruchung die Fahrerleistung potenziell beeinflusste, umfassend zu bewerten. Gleichzeitig wurde eine große Zahl von Mediator- und Moderatorvariablen betrachtet, die beim Fahren im realen Straßenverkehr auftreten (z.B. Einfluss von Kreuzungen, Wetter, etc.). Dieser Ansatz sollte das Verständnis und die externe Validität der Ergebnisse erhöhen und stellt einen wichtigen Schritt hin zu einem vollständigen Modell jener Variablen dar, die entweder zu unangemessen Verhaltensweisen und Unfällen beitragen oder sie reduzieren. Im Rahmen der Dissertation wurden vier Studien durchgeführt, die auf der Grundlage von zwei SHRP 2 Datensätzen die Zusammenhänge zwischen kognitiven und anderen Ablenkungsquellen, Umwelt-, Situations- und Personenfaktoren und Fahrerleistung untersuchten. Weiterhin wurden Kausalfaktoren in 315 vom Fahrer verursachten Unfällen und Beinaheunfällen, die mit Fahrerablenkung, Fahrerbeeinträchtigung oder keinem dieser Faktoren assoziiert waren, analysiert. Die erste Studie untersuchte die Auswirkungen von Wut beim Fahren und Streit mit dem Beifahrer oder jemandem am Telefon auf die Fahrerleistung. Wut beim Fahren ging mit einer Häufung aggressiver Verhaltensweisen einher, jedoch nicht mit einer Erhöhung von Fahrfehlern. Streitgespräche mit dem Beifahrer oder einer Person am Telefon (das heißt, wenn mutmaßlich das höchste Maß an kognitiver Ablenkung vorlag), schienen darüber hinaus mit keiner Form von unangemessenen Verhaltensweisen im Zusammenhang zu stehen. Die zweite Studie untersuchte, wie sich kognitive, visuelle und manuelle Fahrerablenkung, emotionale Beeinträchtigung sowie Umwelt-, Situations- und Persönlichkeitsfaktoren auf die Fahrerleistung auswirken. Ein Zusammenhang zwischen kognitiver Ablenkung und einer Verschlechterung der Fahrerleistung konnte nicht festgestellt werden. Die dritte Studie replizierte und erweiterte Ergebnisse der zweiten Untersuchung auf der Grundlage eines größeren Datensatzes, bestehend aus Fahrsegmenten, die Unfällen, Beinaheunfällen und Baselines vorausgingen und weder emotionale noch andere Fahrerbeeinträchtigungen enthielten. In Übereinstimmung mit den Ergebnissen der ersten und zweiten Studie, wurde keine Assoziation zwischen kognitiver Ablenkung und einer verschlechterten Fahrerleistung festgestellt. Bei der vierten Studie handelte es sich um eine vergleichende Analyse von Risikofaktoren für Unfälle/ Beinaheunfälle, die mit verschiedenen Arten von Ablenkung, Beeinträchtigung oder keinem von beiden, assoziiert waren. Unfälle, denen eine kognitive Ablenkung vorausgegangen war, waren vor allem mit von Ablenkung unabhängigen Fahrfehlern verbunden - genau wie die Unfälle, denen keine beobachtbare Nebentätigkeit vorausgegangen war. Dieses Ergebnis lässt vermuten, dass in früheren „naturalistic driving studies“, das Unfallrisiko von kognitiv ablenkenden Nebentätigkeiten eventuell sogar überschätzt wurde. Zusammenfassend legen die Ergebnisse die Schlussfolgerung nahe, dass kognitive Ablenkung durch beobachtbare emotionale Beeinträchtigung, (überwiegend) kognitiv ablenkende Nebenaufgaben oder die Kombination dieser beiden Faktoren, nicht mit sichtbaren negativen Auswirkungen auf die Fahrerleistung im tatsächlichen Straßenverkehr assoziiert werden kann. Im Gegensatz dazu hatten ablenkende Tätigkeiten, die zu Blickabwendungen von der Straße führen, und solche, die mit einem besonders hohen Unfallrisiko assoziiert werden, die größte Wahrscheinlichkeit Fahrfehler und Unfälle zu verursachen. / Driver distractions are among the leading causes of motor vehicle accidents. Such distractions can stem from competing visual, manual, or cognitive resources, all of which have been associated with detrimental effects on driving performance. Although the negative impacts of visual/visual-manual distraction are widely agreed upon, the effects of cognitive load on driving errors and crash risk are still debated. On the one hand, numerous experimental studies have shown adverse effects of cognitive distraction on driving performance. In contrast, most existing naturalistic driving studies have either not revealed increased crash/near-crash risk due to cognitive distraction, or have even reported a safety benefit. The number of in-vehicle tasks placing cognitive load on the driver is increasing in recent years due to the development of auditory human–machine interfaces such as voice control for several functions. This has enhanced the need to assess how cognitive distraction affects driving performance. These results are necessary to provide society, government, and industry with valid risk estimates, which will affect decision making regarding how to enhance the safety of using in-vehicle human-machine interfaces while driving. Therefore, the main objective of this thesis was to investigate how cognitive distraction affects driving performance. Different types of cognitive distraction that commonly affect most drivers in naturalistic conditions were coded and analyzed in the present thesis, including: cognitively distracting secondary tasks (e.g., talking on the phone, singing), driver emotion (e.g., happiness, anger/frustration, sadness), and combinations of driver emotion and secondary task demand (e.g., arguing with a passenger or with someone on the phone). Environmental, situational, and individual factors cannot be ignored when investigating the effects of cognitive distraction on driving performance, as they are mediating and moderating variables for estimating distraction relative risk in naturalistic driving. Therefore, a holistic approach guided this thesis towards incorporating as many important variables as possible that interact with the engagement in cognitively distracting activities. Data from the largest naturalistic driving study ever conducted (the second Strategic Highway Research Program, SHRP 2) were coded and analyzed to comprehensively assess many situations in which cognitive load potentially affected driving performance. Further, the goal was to simultaneously consider many possible mediating and moderating variables existent in real-world traffic (such as intersection influences, weather, etc.). This approach should increase understanding and external validity of the results, as well as represent an important step towards building a complete model depicting variables that contribute to or mitigate aberrant driving behaviors and crash risk. Four different analyses focused on two SHRP 2 data subsets to assess the relationship between cognitive and other distraction sources, environmental, situational, and individual factors, as well as driving performance. In addition, contributing factors in 315 at-fault crash and near-crash events associated with driver distraction, driver impairment, or neither of the two were analyzed. The first study examined driving performance in relation to driving anger as well as arguing with a passenger or with someone on the phone. Results showed that driving anger was associated with more frequent aggressive driving behaviors without increasing driving error frequency. Furthermore, when a conflict arose with a passenger or with someone on the phone (i.e., when the level of cognitive distraction was expected to be highest), there did not appear to be a link to any type of aberrant driving behavior. The second study analyzed driving performance based on cognitive, visual, and manual driver distraction, emotional impairment, as well as environmental, situational, and individual factors. Cognitive distraction was not associated with any decline in driving performance. The purpose of the third analysis was to replicate and extend the second study’s effects based on a larger data sample of driving segments preceding crashes, near-crashes, and matched baselines, of drivers not exhibiting emotional or other impairment types. Corroborating the first and second study’s results, there was no association between cognitive distractions and impaired driving performance. Finally, the fourth study compared the risk factors of crashes/near-crashes associated with either different driver distraction types, impairment, or neither. Crashes preceded by cognitive distraction were mainly associated with driving errors unrelated to the secondary task demands, as were the crashes preceded by no observable secondary task. This finding suggests that previous studies analyzing naturalistic driving data may have even overestimated the crash risk of cognitively distracting secondary task engagement. In summary, this thesis provides compelling evidence that cognitive distraction, either through observable emotional impairment, (mainly) cognitively distracting secondary tasks, or the combination of both, has no apparent relation with poorer driving performance observable in real-world traffic. On the contrary, distracting activities requiring the driver’s gaze to move away from the forward roadway and those associated with a particularly high crash risk had the highest chances of causing driving errors and crashes.