Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection NavigationOACSTPCDEI

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently.This paper proposes a reinforcement learn-ing(RL)method for autonomous vehicles to navigate unsignal-ized intersections safely and efficiently.The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learn-ing.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections.The algorithm employs experi-ence replay to overcome the on-policy limitation of proximal pol-icy optimization and incorporates the collision risk constraint into the policy optimization problem.The proposed safe RL algo-rithm can balance the trade-off between vehicle traffic safety and policy learning efficiency.Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions.The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driv-ing systems at unsignalized intersections.