大连理工大学学报2025，Vol.65Issue(2)：142-151,10.DOI:10.7511/dllgxb202502005
基于深度强化学习的自动驾驶分段决策方法
Segmentation decision-making method for autonomous driving based on deep reinforcement learning
摘要
Abstract
Based on the road segmentation method,differentiated vehicle control strategies are adopted in different road segments to meet the performance requirements of autonomous driving decision-making systems.Firstly,considering the complex and variable scale characteristics of road features,the road features are mapped to transform coefficients using wavelet transform and Otsu algorithm to achieve adaptive segmentation of the road.Secondly,in order to improve the decision-making ability of the model,the autonomous driving segmentation decision-making task is decomposed into two parallel decision-making subtasks,horizontal and vertical,based on deep reinforcement learning(DRL)and reward decomposition architecture.The segmentation decision-making model and reward function are constructed separately,and an action masking strategy is designed to improve the training speed of the model.Finally,the effectiveness of the proposed model is verified by a series of experiments.The experimental results show that compared with the traditional DRL algorithm,the DRL algorithm with the introduction of reward decomposition architecture and action masking strategy not only ensures reliable decision-making of the driving system,but also improves traffic efficiency,safety and other aspects.关键词
自动驾驶/道路分段/小波分解/深度强化学习Key words
autonomous driving/road segmentation/wavelet decomposition/deep reinforcement learning分类
交通运输引用本文复制引用
王春淇,张明恒,周俊平,姚宝珍,石佳伟..基于深度强化学习的自动驾驶分段决策方法[J].大连理工大学学报,2025,65(2):142-151,10.基金项目
国家自然科学基金资助项目(52272413). （52272413）
