A game-theoretic driver steering model with individual risk perception field generation
提出一个基于博弈论的驾驶员转向模型，通过个体风险感知场量化驾驶风险，并用于模拟驾驶员与自动化系统在转向控制中的交互，实验验证该模型能有效捕捉个体差异和降低预测误差。
Driver-automation shared steering control (SSC) has emerged as a promising technology for enhancing vehicle safety, but desire to achieve seamless collaboration between the driver and automation requires an in-depth understanding of driver steering behavior in interaction with automation. In this paper, we introduce a game-theoretic driver steering model with individual risk perception field generation. Firstly, a driver risk perception field is developed based on a novel concept of potential injury risk (PIR) to provide a quantitative estimation of the driver's perceived driving risk. This approach offers an explicit and physically meaningful structure for simulating the driver's risk perception process and elucidating the reasons for discrepancies in risk perception. Then, this driver risk perception field is integrated into the framework of non-cooperative Nash game to model the steering interaction between the driver and automation, and the analytical expression of Nash equilibrium is derived in detail. The resulting combined driver model effectively captures the driver adaptation at both the control and planning levels. Next, the key parameters of the combined driver model and its comparators are identified using measured driver steering behavior data from thirty subjects in a series of driving simulator experiments. Finally, the effectiveness and superiority of the combined driver model is validated through a comprehensive comparative analysis. The results demonstrate that the combined driver model achieves the lowest prediction errors compared to its comparators and effectively captures the individual differences in risk perception and steering behavior.