Deep reinforcement learning augmented decision model for intelligent driving cars

Abstract

In view of the fact that the state machine decision model cannot effectively handle the rich contextual information and the influence of uncertain factors in ice and snow environments, a deep reinforcement learning agent based on the deep Q network algorithm (DQN) was constructed. The motion planner was used to augment the agent, and the rule-based decision planning module and the deep reinforcement learning model were integrated to establish the DQN-planner model, thereby improving the convergence speed and driving ability of the reinforcement learning agent. Finally, based on the CARLA simulation platform, a comparative experiment was conducted on the driving ability of the DQN model and the DQN-planner model on low-adhesion ice and snow roads, and the training process and verification results were analyzed respectively.