A Hybrid Approach Combining Upper-Level Policy Search and MPC for Lane Change of Autonomous Vehicles

In this paper we present a novel approach to address the lane change maneuver for Autonomous Vehicles (AVs). We frame this challenge as a parametric Model Predictive Control (MPC) problem, recognizing that the successful execution of lane changes requires two critical components: the decision on when to initiate these maneuvers and the actual generation of the maneuvers. Unlike existing approaches that often decouple decision-making and planning tasks, leading to performance bottlenecks and conservative solutions, our approach adopts a hybrid perspective. Specifically, we tackle the problem of capturing the lane change decision-making through an upper-level policy search, which, in turn, guides an MPC policy in generating the maneuver. The proposed approach leverages a weighted maximum likelihood technique for policy learning, effectively optimizing the lane change strategy. Furthermore, we incorporate self-supervised learning techniques to adapt to dynamic and online scenarios, ensuring that the AV can handle unexpected changes in its environment. We provide numerical results demonstrating the effectiveness of the proposed approach, highlighting its potential for improving AV maneuvering in dynamic environments.