A real-time decision-making and motion planning framework with collision avoidance for intelligent vehicle

Safety and real-time performance are key requirements for decision-making and motion planning in intelligent vehicles. However, existing learning-based methods lack interpretability in terms of safety, making it difficult to effectively avoid collision risks. Additionally, rule-based approaches face challenges such as planning failures and limited real-time performance. To address these issues, an integrated framework for real-time decision-making and motion planning focused on collision avoidance is proposed. First, in terms of safety, a vehicle motion-based safety assessment module (VMSA) is developed to provide interpretable safety guarantees. This ensures continuous checking and timely adjustment of lane change gap, effectively preventing planning failures. Second, in terms of real-time performance, a motion planning module based on multi-objective deep reinforcement learning (DRL) and transfer learning is designed to optimize motion trajectory generation. Additionally, a DRL policy based on global traffic features is proposed to optimize lane selection, and curriculum learning is employed to enhance training stability. Simulation results show that, compared with other methods, the proposed framework enables real-time decision-making while effectively improving driving efficiency and safety.