Quantifying the Individual Differences of Drivers' Risk Perception via Potential Damage Risk Model

There will be a time when automated vehicles coexist with human-driven ones. Understanding how drivers assess driving risks and modeling their differences is crucial for developing human-like and personalized behaviors in automated vehicles, gaining people's trust and acceptance. However, existing driving risk models are usually developed at a statistical level, and no single model can accurately describe and explain the variations in risk perception among drivers. We propose a concise yet effective model known as the Potential Damage Risk (PODAR) model, which provides a universal and physically meaningful structure for estimating driving risk and explaining the reasons for differences in risk perception. Leveraging an open-access dataset collected from an obstacle avoidance experiment, this paper establishes individual risk perception models for drivers with high fitness performances. We conclude that the variations in risk perception among drivers stem from their assessments of potential damage, accounting for the uncertainty in both temporal and spatial dimensions. Our findings offer an explanation for human risk perceptions and present a promising risk model for autonomous vehicles to develop human-like behaviors and personalized services.