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.

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.

Safety has been ranked as the number one concern for the acceptance and adoption of automated vehicles since safety has driven some of the most complex requirements in the development of self-driving vehicles. Recent fatal accidents involving self-driving vehicles have uncovered issues in the way some automated vehicle companies approach the design, testing, verification, and validation of their products. Traditionally, automotive safety follows functional safety concepts as detailed in the standard ISO 26262. However, automated driving safety goes beyond this standard and includes other safety concepts such as safety of the intended functionality (SOTIF) and multi-agent safety. Characterizing the Safety of Automated Vehicles addresses the concept of safety for self-driving vehicles through the inclusion of 10 recent and highly relevent SAE technical papers. Topics that these papers feature include functional safety, SOTIF, and multi-agent safety. As the first title in a series on automated vehicle safety, each will contain introductory content by the Editor with 10 SAE technical papers specifically chosen to illuminate the specific safety topic of that book.

The development of automated vehicle (AV) technology is suggesting a promising future of safer and more efficient transportation. However, there are still many challenges in ensuring the operational safety of AVs before their deployment. Scenario-based testing of AVs is an essential part of the safety verification of this technology, and generating challenging scenarios is critical for scenario-based testing of AVs. Research presented in this dissertation focuses on developing a methodology for crash sequence analysis which is used to generate scenarios for AV safety testing. AVs with SAE Driving Automation Levels 3 and 4 are expected to share the roads and handle conflicts with human drivers. Building a scenario library based on comprehensive samples of historical crash data would be the most efficient way to set up the foundation of a scenario-based AV verification system. Crash scenarios are temporally ordered scenes that consist of 1) participants' actions and interactions, and 2) the relatively static surrounding environment. To incorporate both elements, this dissertation's scenario-generating procedure included two steps - 1) characterization of crashes based on sequences of events, and 2) specification of interrelationships between crash sequences and other crash attributes that depict the surrounding environment. Research tasks developed and demonstrated the crash-sequence-based scenario-generating procedure with three studies.In the first study, a first-of-its-kind crash sequence analysis methodology was developed to serve as the foundation of this dissertation research. In the second study, crash sequence analysis methods were applied to California AV collision data to query and identify representative crash sequence types. In the third study, the scenario-generating procedure incorporates a sequence analysis and a Bayesian network analysis. This dissertation contributes to the understanding of traffic crashes and efficient testing of AVs by developing a first-of-its-kind crash sequence analysis methodology and a novel test scenario generating procedure. This dissertation laid the foundation for traffic crash sequence analysis and the use of crash data for AV test scenario generation. As future crashes happen, new data can be added to the database to add greater depth and further understanding to the critically important topic of scenario-based AV safety evaluation. Findings from this dissertation will have further influences in improving transportation safety and supporting the transition into automated transportation. Knowledge of crash sequences will help future research in analyzing crash causations. A comprehensive test scenario library will speed up large-scale AV safety testing with the help of simulation.

RAND researchers analyzed three approaches to assessing the safety of automated vehicles (AVs)--measurements, processes, and thresholds--and how they interact. Researchers also explored the elements of effective communications regarding AV safety.

Automotive Vehicle Safety is a unique academic text, practical design guide and valuable reference book. It provides information that is essential for specialists to make better-informed decisions. The book identifies and discusses key generic safety principles and their applications and includes decision-making criteria, examples and remedies. It

Up-To-Date Coverage of Every Aspect of Commercial Aviation Safety Completely revised edition to fully align with current U.S. and international regulations, this hands-on resource clearly explains the principles and practices of commercial aviation safety—from accident investigations to Safety Management Systems. Commercial Aviation Safety, Sixth Edition, delivers authoritative information on today's risk management on the ground and in the air. The book offers the latest procedures, flight technologies, and accident statistics. You will learn about new and evolving challenges, such as lasers, drones (unmanned aerial vehicles), cyberattacks, aircraft icing, and software bugs. Chapter outlines, review questions, and real-world incident examples are featured throughout. Coverage includes: • ICAO, FAA, EPA, TSA, and OSHA regulations • NTSB and ICAO accident investigation processes • Recording and reporting of safety data • U.S. and international aviation accident statistics • Accident causation models • The Human Factors Analysis and Classification System (HFACS) • Crew Resource Management (CRM) and Threat and Error Management (TEM) • Aviation Safety Reporting System (ASRS) and Flight Data Monitoring (FDM) • Aircraft and air traffic control technologies and safety systems • Airport safety, including runway incursions • Aviation security, including the threats of intentional harm and terrorism • International and U.S. Aviation Safety Management Systems