Cooperative decision making for connected automated vehicles in multiple driving scenarios

To improve the application range of decision-making systems for connected automated vehicles, this paper proposes a cooperative decision-making approach for multiple driving scenarios based on the combination of multi-agent reinforcement learning with centralized planning. Specifically, the authors derived driving tasks from driving scenarios and computed the policy functions for different driving scenarios as linear combinations of policy functions for a set of specific driving tasks. Then, the authors classified vehicle coalitions according to the relationships between vehicles and used centralized planning methods to determine the optimal combination of actions for each coalition. Finally, the authors conducted tests in two driving scenarios considering different traffic densities to evaluate the performance of the developed approach. Simulation results demonstrate that the proposed approach exhibits good robustness in multiple driving scenarios while enabling cooperative decision making for connected automated vehicles, thereby ensuring safe and rational decision making.