Decision-Making Models Based on Meta-Reinforcement Learning for Intelligent Vehicles at Urban Intersections

Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology. The problem is that existing decision-making algorithms cannot effectively deal with complex random scenarios at urban intersections. To deal with this, a deep deterministic policy gradient (DDPG) decision-making algorithm (T-DDPG) based on a time-series Markov decision process (T-MDP) was developed, where the state was extended to collect observations from several consecutive frames. Experiments found that T-DDPG performed better in terms of convergence and generalizability in complex intersection scenarios than a traditional DDPG algorithm. Furthermore, model-agnostic meta-learning (MAML) was incorporated into the T-DDPG algorithm to improve the training method, leading to a decision algorithm (T-MAML-DDPG) based on a secondary gradient. Simulation experiments of intersection scenarios were carried out on the Gym-Carla platform to verify and compare the decision models. The results showed that T-MAML-DDPG was able to easily deal with the random states of complex intersection scenarios, which could improve traffic safety and efficiency. The above decision-making models based on meta-reinforcement learning are significant for enhancing the decision-making ability of intelligent vehicles at urban intersections.