  There will be a long time when automated vehicles are mixed with human-driven
vehicles. Understanding how drivers assess driving risks and modelling their
individual differences are significant for automated vehicles to develop
human-like and customized behaviors, so as to gain people's trust and
acceptance. However, the reality is that existing driving risk models are
developed at a statistical level, and no one scenario-universal driving risk
measure can correctly describe risk perception differences among drivers. We
proposed a concise yet effective model, called Potential Damage Risk (PODAR)
model, which provides a universal and physically meaningful structure for
driving risk estimation and is suitable for general non-collision and collision
scenes. In this paper, based on an open-accessed dataset collected from an
obstacle avoidance experiment, four physical-interpretable parameters in PODAR,
including prediction horizon, damage scale, temporal attenuation, and spatial
attention, are calibrated and consequently individual risk perception models
are established for each driver. The results prove the capacity and potential
of PODAR to model individual differences in perceived driving risk, laying the
foundation for autonomous driving to develop human-like behaviors.
