Download or read book Traffic Crash Patterns and Causations Based on Sequence of Events written by Yu Song and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.