XAI-Informed Comparative Safety Performance Assessment of Human-Driven Crashes and Automated Vehicle Failures

Current Automated Vehicle (AV) technologies still face challenges in operating safely across diverse road environments, as existing infrastructure is not yet fully adapted to AV-specific requirements. While many previous studies have relied on simulations, real-world data is crucial for accurately assessing AV safety and understanding the impact of road characteristics. To address this gap, this study analyzes human-driven vehicle (HDV) crashes and AV failures using machine learning and explainable AI (XAI), providing insights into how road design can be improved to facilitate AV integration into existing infrastructure. Using XGBoost-based frequency modeling, the study achieved accuracy ranging from 0.6389 to 0.9770, depending on the specific model. The findings indicate that road geometry and traffic characteristics play a significant role in road safety, while the impact of road infrastructure varies across different road classifications. In particular, traffic characteristics were identified as key contributors to HDV crashes, whereas road geometry was the most critical factor in AV failures. By leveraging real-world AV failure data, this study overcomes the limitations of simulation-based research, improving the reliability of safety assessments. It provides a comprehensive evaluation of road safety across different road types and traffic flow conditions while simultaneously analyzing HDV crashes and AV failures. The findings offer critical insights into the challenges of mixed-traffic environments, where AVs and HDVs must coexist, highlighting the need for adaptive road design and infrastructure strategies to enhance safety for all road users.