Neural MPC-Based Decision-Making Framework for Autonomous Driving in Multi-Lane Roundabout.

The multi-lane roundabout poses significant challenges for autonomous driving due to its complex road structure and traffic conditions. To address these challenges, this paper proposes a novel Neural Model Predictive Control (NMPC)-based decision-making framework that integrates prediction, planning, and control for autonomous vehicles to navigate multi-lane roundabouts. The proposed NMPC framework learns a dynamical model, incorporating interaction data, to accurately predict the behavior of surrounding traffic participants. Multiple candidate static paths are then generated based on the road structure, and the decision-making problem is formulated as a series of parallel static path tracking control problems subject to safety constraints. The static path with minimal tracking cost is selected as the target reference path, and the tracking control is generated simultaneously. To enhance computational efficiency, NMPC utilizes a critic network and an actor network to approximate the tracking cost and the control policy, respectively. Experimental evaluation on a multi-lane roundabout simulator, based on a real roundabout in Beijing, demonstrates that the proposed method performs better in terms of driving safety and efficiency compared to several baseline algorithms across various traffic densities.