Quantitative Analysis of Driving Environment Factors Affecting Takeover Time in Conditional Autonomous Driving Systems

Understanding the conditions that affect takeover time (TOT) in conditional autonomous driving systems remains a challenging issue. The takeover process requires a seamless transition of control from the autonomous system to the driver when the system encounters situations it cannot manage. This study examines the effects of traffic conditions, road geometry, and weather on TOT using a linear mixed model to quantify their influence. Preliminary findings indicate that factors such as rain, gender, and age significantly extend control transition duration. These insights highlight the need for personalized designs in automated driving systems (ADSs) and takeover request protocols to accommodate diverse driver characteristics and environmental conditions. While the research utilizes a driving simulator, suggesting the need for field validation, it offers a foundational understanding that can enhance the safety and efficiency of conditional automation systems. This study contributes to safer ADS design and supports the commercial viability of conditional autonomous vehicles.