Stackelberg Differential Lane Change Game Based on MPC and Inverse MPC

Qingyu Zhang, Reza Langari, H. Eric Tseng, Shankar Mohan, Steven Szwabowski, Dimitar P. Filev

Abstract: A Stackelberg differential game theoretic model predictive controller is proposed for an autonomous highway driving problem. The hierarchical controller’s high-level component is the two-player Stackelberg differential lane change game, where each player uses a model predictive controller (MPC) to control his/her own motion. The differential game is converted into a bi-level optimization problem and is solved with the branch and bound algorithm. Additionally, an inverse MPC algorithm is developed to estimate the weights of the MPC cost function of the target vehicle. The low-level hybrid MPC controls both the autonomous vehicle’s longitudinal motion and its real-time lane determination. Simulations indicate both the inverse MPC’s capability on aggressiveness estimation of target vehicles and DGTMPC’s superior performance in interactive lane change situations.