Robustness-enhanced cooperative adaptive cruise control for multi-task scenarios via generalised joint multi-agent reinforcement learning

Cooperative adaptive cruise control (CACC) leverages vehicle-to-vehicle communication to achieve tighter distance control and better formation maintenance, improving efficiency and safety. However, cross-task robustness and multi-objective decision-making remain challenging. This paper introduces a Multi-Agent Reinforcement Learning (MARL) framework tailored for multi-objective CACC in cross-task environments. The proposed approach employs a synergistic cognitive fusion and dynamic weight adaptation strategy to optimize the allocation of multiple driving objectives. By dynamically adjusting the relative importance of objectives such as safety, efficiency, and comfort, the framework adapts to varying driving scenarios. Simulation experiments demonstrate the method's effectiveness in enhancing overall system performance and driving safety. Furthermore, comparisons with real-world driving data underscore the approach's potential for practical application.