Interaction and Risk-Aware Spatio-Temporal Trajectory Planning for Autonomous Driving Considering Game Interaction

Aiming at the trajectory planning problem of autonomous vehicles, a spatio-temporal joint planning framework considering both multi-vehicle interactions through a game theoretic approach and asymmetric risk field theory was proposed in this article. Through game theoretic forward propagation, the predicted future trajectory of the surrounding vehicle is acquired and coupled into the framework of the ego vehicle decision-making and planning, so that the ego vehicle trajectory planning considering multi-vehicle interaction can be realized. The trajectory is derived from a spatio-temporal planning approach to integrate the velocity planning and path planning and the safety of the trajectory is guaranteed. Furthermore, the trajectory points generated by forward propagation can effectively consider the asymmetric risk field generated by surrounding vehicles and integrate it into the solution of the numerical optimization problem, comprehensively considering the impact of different types of surrounding vehicles, together with their states and other factors, so that the calculated route is safer and in line with human decision-making. We further leverage the prospect theory to mimic the behavior of real human drivers, considering irrational factors of human decision processes. The simulation results show that in the dense traffic flow with frequent interactions with surrounding vehicles, the autonomous ego vehicle can reasonably change lanes and achieve efficient and safe driving.