MODELING AND IDENTIFYING FACTORS ASSOCIATED WITH FATAL CRASHES INVOLVING VEHICLES WITH ADVANCED DRIVER ASSISTANCE SYSTEMS

Recent advancements in vehicular technology aim to enhance traffic safety by warning drivers or automating driving tasks. Driver warning systems (DWSs) alert drivers to unsafe situations. Advanced driver assistance systems (ADASs) can actively control acceleration, braking, and steering, reducing the reliance on human drivers. Although vehicles with DWS and ADAS are expected to enhance safety, the effectiveness of these systems in real-world driving conditions with varying traffic and vehicle interactions remains a knowledge gap. This dissertation provides an analysis framework to identify factors influencing fatal crashes involving vehicles with varying DWSs and ADASs. The objectives include evaluating data on vehicles with various DWSs and ADASs, comparing factors affecting fatal crashes involving vehicles with and without these systems, and examining the influence of traffic and vehicle characteristics on safety. Logistic regression models are employed to analyze the data and identify factors affecting fatal crashes, considering different DWSs, ADASs, and crash types. The findings from this research contribute to improving traffic safety by enhancing the understanding of factors that influence fatal crashes involving vehicles with DWSs and ADASs. The results will assist in developing effective strategies to mitigate risks, improve the design of these technologies, and facilitate infrastructure planning for future adoption.