Abstract:

The decision-making system can integrate environment and ego vehicle information, so that the autonomous vehicle produces safe and reasonable driving behavior, which is the core technology to realize the autonomous driving. Reinforcement learning algorithm adopts a self-supervised learning method, so that the decision-making system of autonomous vehicles can autonomously learn the optimal decision model through continuous improvement of its strategy during the interaction with the environment, which provides a direction for building an effective decision-making system.This study summarizes the research progress in recent years of the decision-making method based on reinforcement learning in terms of improving decision accuracy, improving decision-making breadth, and dealing with uncertain factors. The improvement of decision-making accuracy mainly depends on the introduction of deep learning algorithm with strong representation ability and the hierarchical abstraction technology that can decompose complex tasks to alleviate the dimension disaster. The uncertainty is considered by partially observable Markov decision process to improve driving safety.

Key words: autonomous driving, reinforcement learning, decision-making, self-monitoring learning, strategy improvement, decision accuracy, decision breadth, uncertainty