Scalable Game-Theoretic Decision-Making for Self-Driving Cars at Unsignalized Intersections
Mingfeng Yuan, Jinjun Shan, Hunter Schofield
Abstract
Sharing the road with human drivers requires autonomous vehicles to account for interactions between them. To resolve traffic conflicts in unsignalized intersections, a robust adaptive game-theoretic decision-making algorithm with scalability is proposed based on the receding horizon optimization, level-k game theory, and switching directed graph. A mismatch between the inherent (k-1) assumption of level-k theory and actual driver type may lead to unsafe action selection and reduce driving safety. To handle this problem, in this work, an autonomous vehicle would predict the driver types of surrounding vehicles based on historical interactive behaviors between them and utilize its trust in the driver types to achieve an adaptive driving strategy. Besides, switching interaction graph is incorporated into an adaptive level-k framework for the first time, so as to cut off the connection between ego vehicle and nearby vehicles that do not affect driving behavior of the former, contributing to reducing the computing complexity. The feasibility, effectiveness, and real-time implementation of the proposed method are validated on both hardware and ROS-Gazebo platform.