Will Automated Vehicles be Safer than Conventional Vehicles? One of the critically important questions that has emerged about advanced technologies in transportation is how to test the actual effects of these advanced systems on safety, particularly how to evaluate the safety of highly automated driving systems. Richard Young's Critical Analysis of Prototype Autonomous Vehicle Crash Rates does a deep dive into these questions by reviewing and then critically analyzing the first six scientific studies of AV crash rates.

Highly automated vehicles or autonomous vehicles are projected to reform the transportation system and improve transportation safety by eliminating drivers’ errors, as safety studies reveal humans contribute to more than 90% of the crashes. This dissertation examines the full spectrum of automated vehicle topics, including related literature, enacted state policies, and manufacturer-reported crash and disengagements reports to understand more about this emerging and transformative technology. First, an extensive review of safety-related literature was conducted, followed by a text analysis to identify areas of research needed to advance the knowledge of automated vehicles. California is the only transparent state as the Department of Motor Vehicles requires permit-holding manufacturers to publicly share automated vehicle safety performance data – crashes, disengagements, and automated vehicle miles traveled – of the vehicles on the public roadways. By harnessing this significant and emerging field data, associated risk factors of crashes and disengagements were identified and quantified using rigor statistical analyses. Contributing roadway, environmental, and vehicle factors were modeled with the most frequent type of crash, rear-end, and stated injury crashes. Disengagements, the transition from autonomous mode to conventional mode, are considered near-miss crashes as the vehicle operator is mitigating the likelihood of a crash by regaining control of the vehicle. These safety-critical events of disengagements were then identified and quantified by using the 5 W’s – who (disengagement initiator), when (the maturity of automated driving system, where (location of disengagement), what and why (the facts causing the disengagement). The assessment of the effects of time on disengagement frequency at the manufacturer- level was conducted. With the rapid technological advancements in automated vehicles, the technology poses a threat to crafting legislation. By using a novel approach of conducting a manual review and synthesis against the only federal guidance available for states, USDOT Model State Policy, married with a text mining analysis, insights on uneven research, development, and deployment of automated vehicle technologies at the state level were revealed. Finally, the implications of the findings and future research areas are discussed to a comprehensive understanding of automated vehicle associated risk factors on the transportation network.

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 edited book comprises papers about the impacts, benefits and challenges of connected and automated cars. It is the third volume of the LNMOB series dealing with Road Vehicle Automation. The book comprises contributions from researchers, industry practitioners and policy makers, covering perspectives from the U.S., Europe and Japan. It is based on the Automated Vehicles Symposium 2015 which was jointly organized by the Association of Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Ann Arbor, Michigan, in July 2015. The topical spectrum includes, but is not limited to, public sector activities, human factors, ethical and business aspects, energy and technological perspectives, vehicle systems and transportation infrastructure. This book is an indispensable source of information for academic researchers, industrial engineers and policy makers interested in the topic of road vehicle automation.

This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.