Due to the dense traffic flow and stochastic uncertainty of vehicle behaviors,the scenario of unsignalized intersection poses significant challenges for autonomous driving.An innovative approach for autonomous driving decision-making at unsignalized intersections is proposed based on the MAPPO(Multi-Agent Proximal Policy Optimization)algorithm.Applying the MetaDrive simulation platform to construct a multi-agent simulation environment,we design a reward function that comprehensively considers traffic regulations,safety including arriving safely and occurring collisions,and traffic efficiency considering the maximum and minimum speeds of vehicles at intersections,aiming to achieve safe and efficient autonomous driving decisions.Simulation experiments demonstrate that the proposed decision-making approach exhibits superior stability and convergence during training compared to other algorithms,showcasing higher success rates and safety levels across varying traffic densities.These findings underscore the significant potential of the autonomous driving decision-making solution for addressing challenges in unsignalized intersection environments,thereby advancing research in autonomous driving decision-making under complex road conditions.
维普
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