Interaction-Aware Decision-Making for Autonomous Vehicles
Yongli Chen, Shen Li, Xiaolin Tang, Kai Yang, Dongpu Cao, Xianke Lin
Abstract
Complex, dynamic, and interactive environment brings huge challenges to autonomous driving technologies. Because of the strong interactions between different traffic participants, autonomous vehicles (AVs) must learn how to interact with other road users. Failure to consider interaction when making decisions may result in safety issues. In this article, an interaction-aware decision-making approach is proposed for AVs. First, focusing on the interaction at uncontrolled midblock crosswalks, the game theory is used to model the vehicle–pedestrian interaction (VPI). Then, an interaction inference framework is developed using the interaction model to obtain interaction information with pedestrians. Besides, a collaborative action planning method is proposed to generate collaborative actions. More importantly, interactive decision-making is formulated as an optimization problem by considering the task item and action item. Furthermore, considering pedestrians’ different levels of cooperation, the social force pedestrian model is developed. Then, a highly interactive environment is constructed. Finally, qualitative and quantitative evaluations are carried out against three baseline methods. The result shows that our method can interact with different pedestrians and balance safety and efficiency compared to baseline methods.