When Do Drivers Maneuver: Experimental Investigation and Inference of Perception-Response Time for Tailored Safety Systems in Intelligent Vehicles

Sudden traffic hazards trigger collision-avoidance behaviors in drivers that can significantly impact vehicle dynamics, potentially conflicting with the existing advanced driver assistance systems (ADAS), such as autonomous emergency braking and steering. This behavior can lead to unexpected vehicle movements, further complicating the situation and elevating the risk of accidents. Understanding and tailoring the inference of drivers’ perception-response time (PRT) is essential for optimizing ADAS activation in intelligent vehicles. This approach allows customization for individual drivers, improving safety and ensuring that interventions are personalized and minimally disruptive to normal driving patterns. To achieve this objective, this study performs high-fidelity simulation experiments to gather a comprehensive multidimensional dataset on drivers’ responses in safety-critical scenarios, primarily focusing on PRT and its influencing factors. Using the collision-avoidance behavior data, a driver evidence accumulation model is created to explain PRT distribution and facilitate real-time personalized inferences. We also analyze the relationship between model parameters and real-world physical significance, demonstrating that driver decisions rely on visual evidence accumulation influenced by dynamic interactions in different scenarios. Our proposed model, by offering a detailed understanding of drivers’ perceptual and decision-making processes, aids in developing personalized driver assistance system activation recommendations. This approach seeks to create personalized and adaptive systems within intelligent vehicles, thereby reducing human-machine conflicts and improving the overall safety of intelligent transportation systems.