While human drivers are currently responsible for almost all of the transport of goods and people, autonomous vehicles are near-at-hand and are expected to replace human drivers in the next few decades. Several vehicle manufacturing and tech companies have begun testing their autonomous vehicles on public roads. During this period of testing, human drivers are employed to ensure the safety of the passengers and those in the immediate vicinity in the event of a failure of automation. Moreover, lower levels of automation, that are currently available to the general public, also require constant human supervision to ensure safety. This makes the study of the cognitive, behavioral and psycho-physiological state of drivers of automation crucial. In this thesis, I review existing literature on the study of driver state and behavior and on the methodologies employed by researchers to study them. I present a series of studies that investigate the changes in driver state while using automated vehicles. To this end, I employed various physio- logical sensors such as near-infrared spectroscopy, galvanic skin response, and electrocardiography. I argue the case for driver state monitoring in automated vehicles to ensure vigilance and driver availability for a take over of control. The first study I present investigates the reasons for the presence of sleepy and drowsy behavior in drivers of automated vehicles. I found that the reason for drowsy behavior was prolonged intervals of low cortical activity. The second and third study investigate the changes in cortical activity across different levels of automation and over time. These studies showed that drivers who used partially automated vehicles had the highest levels of cognitive activity and drivers of partial and fully automated vehicles showing significant decreases in cortical activity over time. In the last study, I employ a panel design to investigate the impact of prior and knowledge and training on driver preparedness and behavior. I found that drivers are prepared when they are trained on the possible failure modes of automation. However, this preparedness decreases over time in both drivers with and without any training. These studies add to the existing knowledge of driver state investigations while providing much needed insight into the longitudinal changes in driver state. It is my hope that this thesis provides the foundation for future investigations into training programs for safety drivers in autonomous vehicles and serve as guidance for developers of automated driving technology.

This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".

The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.

This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty.

This book presents works from world-class experts from academia, industry, and national agencies representing countries from across the world focused on automotive fields for in-vehicle signal processing and safety. These include cutting-edge studies on safety, driver behavior, infrastructure, and human-to-vehicle interfaces. Vehicle Systems, Driver Modeling and Safety is appropriate for researchers, engineers, and professionals working in signal processing for vehicle systems, next generation system design from driver-assisted through fully autonomous vehicles.

This comprehensive 2nd edition covers the key issues that relate human behavior to traffic safety. In particular it covers the increasing roles that pedestrians and cyclists have in the traffic system; the role of infotainment in driver distraction; and the increasing role of driver assistance systems in changing the driver-vehicle interaction.