A unified risk field-based driving behavior model for car-following and lane-changing behaviors simulation

The modeling of driving behavior is pivotal for the accurate simulation of traffic scenarios and for providing human-like decision-making of autonomous driving systems. Car-following (CF) and lane-changing (LC) behaviors are continuous maneuvers within traffic flow, generally modeled separately in the literature. The coherence between these two behaviors may be ignored, leading to unrealistic behavioral simulations. Therefore, this paper establishes a risk field-based driving behavior model for two-dimensional motion, ensuring coherent modeling of CF and LC behaviors under a unified framework. First, a risk quantification method is developed to calculate the risk in two-dimensional scenarios, accounting for risk over the preview time. A cubic polynomial is applied to generate path curves that align with vehicle dynamics. Second, the enhanced behavior model primarily comprises two integral components: path and trajectory planning. These two components aim to identify the path or trajectory that maximizes the benefit while meeting the desired risk. Third, the maximum acceptable risk, representing a higher risk than the desired risk, is defined to facilitate path adjustment and avoid frequent path adjustment. Finally, the proposed model is proved through comparisons with existing models using driving data. Several cases are employed for further analysis to show the model's rationality and potential in various aspects. This study develops the previous risk field-based behavior model from one-dimensional to two-dimensional scenarios, furnishes a unified framework for elucidating driving behavior in various scenarios, and contributes to the progress of behavior modeling.