Every year, approximately 34,000 individuals are fatally injured in crashes on US roads [1]. These fatalities occur across many types of crash scenarios, each with its own causation factors. One way to prioritize research on a preventive technology is to compare the number of occupant fatalities relative to the total number of occupants involved in a crash scenario. Four crash modes are overrepresented among fatalities: single vehicle road departure crashes, control loss crashes, cross-centerline head-on crashes, and pedestrian/cyclist crashes [2]. Interestingly, three of these crash scenarios require the subject vehicle to depart from the initial lane of travel. Lane departure warning (LDW) systems track the vehicle lane position and can alert the driver through audible and haptic feedback before the vehicle crosses the lane line. Lane departure prevention (LDP) systems can perform an automatic steering maneuver to prevent the departure. Another method of prioritizing research is to determine factors common among the fatal crashes. In 2017, 30.4% of passenger vehicle crash fatalities involved a vehicle rollover [1]. Half of all fatal single vehicle road departure crashes resulted in a rollover yet only 12% of fatal multi-vehicle crashes involved a rollover [1]. These often occur after the driver has lost control of the vehicle and departed the road. Electronic stability control (ESC) can provide different braking to each wheel and allow the vehicle to maintain heading. While ESC is a promising technology, some rollover crashes still occur. Passive safety systems such as seat belts, side curtain airbags, and stronger roofs work to protect occupants during rollover crashes. Seat belts prevent occupants from moving inside the occupant compartment during the rollover and both seat belts and side curtain airbags can prevent occupants from being ejected from the vehicle. Stronger roofs ensure that the roof is not displaced during the rollover and the integrity of the occupant compartment is maintained to prevent occupant ejection. The focus of this dissertation is to evaluate the effectiveness of vehicle-based countermeasures, such as lane departure warning and electronic stability control, for preventing or mitigating single vehicle road departure crashes, cross-centerline head-on crashes, and single vehicle rollover crashes. This was accomplished by understanding how drivers respond to both road departure and cross-centerline events in real-world crashes. These driver models were used to simulate real crash scenarios with LDW/LDP systems to quantify their potential crash reduction. The residual crashes, which are not avoided with LDW/LDP systems or ESC, were analyzed to estimate the occupant injury outcome. For rollover crashes, a novel injury model was constructed that includes modern passive safety countermeasures such as seat belts, side curtain airbags, and stronger roofs. The results for road departure, head-on, and control loss rollover crashes were used to predict the number of crashes and injured occupants in the future. This work is important for identifying the residual crashes that require further research to reduce the number of injured crash occupants. / Doctor of Philosophy / Every year in the US, approximately 34,000 individuals are fatally injured in many different types of crashes. However, some crash types are more dangerous than other crash types. Drift-out-of-lane (DrOOL) road departure crashes, control loss road departure crashes, head-on crashes, and pedestrian crashes are more likely to result in an occupant fatality than other crash modes. In three of these more dangerous crash types, the vehicle departs from the lane before the crash occurs. Lane departure warning (LDW) systems can detect when the vehicle is about to cross the lane line and notify the driver with beeping or vibrating the steering wheel. A different system, called lane departure prevention (LDP), can provide automatic steering to prevent the vehicle from leaving the lane or return lane. In control loss crashes, the vehicle's motion is in a different direction than the vehicle's heading. During control loss, it is easier for the vehicle to roll over which is very dangerous. Electronic stability control (ESC) can prevent control loss by applying selective braking to each tire to keep the vehicle's motion in the same direction as the vehicle's heading. If a rollover still occurs, vehicles are equipped with passive safety systems and designs such as seat belts, side curtain airbags, and stronger roofs to protect the people inside. Seat belts can prevent occupants from striking the vehicle interior during the rollover and both seat belts and side curtain airbags can prevent occupants from being ejected from the vehicle. Stronger roofs ensure that the roof is not displaced during the rollover to prevent occupants from being ejected from the vehicle. The focus of this dissertation is to estimate how many crashes LDW, LDP, and ESC systems could prevent. This was accomplished by understanding how drivers respond after leaving their lane in real crashes. Then, these real crash scenarios were simulated with an LDW or LDP system to estimate how many crashes were prevented. The occupants of residual crashes, which were not prevented by the simulated systems, were analyzed to estimate the number of occupants with at least one moderate injury. Understanding which crashes and injuries that were not prevented with this technology can be used to decide where future research should occur to prevent more fatalities in road departure, head-on and control loss crashes.

The transportation sector has been identified as one of many sources of today's energetic and environmental problems. With constantly increasing numbers of vehicles on the road, non-renewable fossil fuels are becoming scarce and expensive. In addition, due to the pollutant emissions of internal combustion engines, the transportation sector is a major producer of greenhouse gas emissions. To resolve these problems researcher are looking for technological solutions, such as more efficient components and alternative drive train technologies, on one hand. On the other hand, work is being done to ensure the most efficient utilization of available technological resources. Eco driving is one way to immediately reduce a driver's energy consumption. In this thesis the potential gains of eco driving for passenger vehicles will be discussed. The main objective of this work is to, first, identify and compare drive train specific, optimal vehicle operation. Secondly, the effect of real-life constraints on potential gains of eco driving is evaluated. In addition, an approach to integrate mathematical optimization algorithms in an advanced driver assist system for eco driving is proposed. Physical vehicle models are developed for three representative vehicles: the conventional, electric and power-split hybrid vehicle. Using real-life and standard drive cycles a baseline mission is defined by specifying trip and road constraint. Applying the dynamic programming algorithms the trajectory optimization problem is solved, minimizing energy consumption for the trip. The effect of traffic on potential gains of eco driving is discussed, considering a vehicle following situation. Integrating emission constraints in the optimization algorithm the environmental advantages of eco driving are discussed. Finally, the developed algorithms were integrated in a driver assist system. Experimental tests on a driving simulator were used to verify the effectiveness of the system, as well as driver acceptance.

Pour résoudre les problèmes environnementaux et énergétiques liés au nombre croissant de véhicules en circulation, deux approches sont envisageables : l'une est technologique et vise à améliorer les composants du véhicule ou son architecture, l'autre est comportementale et cherche à changer la manière d'utiliser les véhicules. Dans ce contexte, l'éco-conduite représente une méthode, applicable immédiatement, permettant à chaque conducteur de réduire sa consommation. L'objectif de cette thèse est donc l'analyse des gains potentiels de l'éco-conduite pour les différents types de véhicules existant : thermique, électrique et hybride. Ainsi, la première partie de ce travail se focalise sur une étude théorique visant à calculer les gains potentiels et à déterminer les règles d'éco-conduite, avant d'aborder dans un second temps une mise en situation plus réaliste et une intégration des algorithmes dans un système d'assistance pour le conducteur. En s'appuyant sur une modélisation énergétique des différents types de véhicules, la détermination et la comparaison du fonctionnement optimal se base sur l'optimisation du profil de vitesse pour des trajets connus. La programmation dynamique a été mise enoeuvre pour calculer la trajectoire optimale énergétique en tenant compte de la contrainte temporelle afin de ne pas pénaliser l'intérêt d'une conduite économe. Evidemment, l'intégration de l'éco-conduite doit, d'une part, tenir compte du trafic à proximité du véhicule et d'autre part, ne pas aboutir à une augmentation des émissions de polluants. Ainsi, en nous appuyant sur des modèles de suivi de véhicules (trafic), nous avons montré que les principes d'éco-conduite restent valables et conduisent de toute façon à des gains énergétiques. Concernant les contraintes d'émissions, des résultats expérimentaux nous ont conduit à adapter nos algorithmes pour répondre simultanément aux aspects écologiques et économiques. Enfin, les connaissances acquises ont été appliquées à la conception d'un système d'assistance testé sur un simulateur de conduite. / The transportation sector has been identified as one of many sources of today's energetic and environmental problems. With constantly increasing numbers of vehicles on the road, non-renewable fossil fuels are becoming scarce and expensive. In addition, due to the pollutant emissions of internal combustion engines, the transportation sector is a major producer of greenhouse gas emissions. To resolve these problems researcher are looking for technological solutions, such as more efficient components and alternative drive train technologies, on one hand. On the other hand, work is being done to ensure the most efficient utilization of available technological resources. Eco driving is one way to immediately reduce a driver's energy consumption. In this thesis the potential gains of eco driving for passenger vehicles will be discussed. The main objective of this work is to, first, identify and compare drive train specific, optimal vehicle operation. Secondly, the effect of real-life constraints on potential gains of eco driving is evaluated. In addition, an approach to integrate mathematical optimization algorithms in an advanced driver assist system for eco driving is proposed. Physical vehicle models are developed for three representative vehicles: the conventional, electric and power-split hybrid vehicle. Using real-life and standard drive cycles a baseline mission is defined by specifying trip and road constraint. Applying the dynamic programming algorithms the trajectory optimization problem is solved, minimizing energy consumption for the trip. The effect of traffic on potential gains of eco driving is discussed, considering a vehicle following situation. Integrating emission constraints in the optimization algorithm the environmental advantages of eco driving are discussed. Finally, the developed algorithms were integrated in a driver assist system. Experimental tests on a driving simulator were used to verify the effectiveness of the system, as well as driver acceptance.