Analysis of Car-Following Safety Evolution in Mixed-Driving Environment: A Resilience Perspective

Abstract

As autonomous-driving technology advances, autonomous vehicles (AVs) and human-driven vehicles (HVs) are expected to coexist for an extended period. While resilience is widely applied in macro safety analysis, its application in micro-process modeling remains underdeveloped. This study proposes a resilience-based framework comprising three phases (initial, safety decay, safety recovery) to investigate the evolution of driving risk across three car-following scenarios: HV–HV (HV following HV), HV–AV (HV following AV), and AV–HV (AV following HV). The analytical framework consists of three steps: First, car-following pairs with complete risk-evolution processes were identified. Second, cluster analysis was applied to categorize phase-specific patterns. Third, the association rule mining algorithm was used to trace risk-evolution chains, followed by a comprehensive evaluation integrating safety and efficiency. Key findings include: (1) significant and rapid safety decay being accompanied by swift recovery, and the following vehicle exhibiting significant feature differences across recovery patterns; (2) safety decay in HV–AV being slower than that in HV–HV, with HV–AV demonstrating a close and conservative driving strategy during the risk-evolution process, with HV–AV meanwhile performing worse in safety and efficiency compared with HV–HV, highlighting the interaction discordance between HV and AV; (3) excessive deceleration of HV being the primary trigger for safety degradation in AV–HV, and AV demonstrating effective adaptation to safety decay, promoting slight safety reduction with rapid recovery, thereby balancing safety and efficiency. These findings reveal principles of risk evolution in mixed-driving environments, providing a novel analytical framework for mixed-driving safety.