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Deep Reinforcement Learning-Based Driving Policy at Intersections Utilizing Lane Graph Networks | |
Liu, Yuqi1,2![]() ![]() ![]() | |
发表期刊 | IEEE Transactions on Cognitive and Developmental Systems
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ISSN | 2379-8920 |
2024-04-02 | |
页码 | 1 - 16 |
摘要 | Learning an efficient and safe driving strategy in a traffic-heavy intersection scenario and generalizing it to different intersections remains a challenging task for autonomous driving. This is because there are significant differences in the structure of roads at different intersections, and autonomous vehicles need to learn to generalize the strategies they have learned in the training environments. This requires the ego vehicle to capture not only the interactions between agents but also the relationships between agents and the map effectively. To address this challenge, we present a technique that integrates the information of high-definition (HD) maps and traffic participants into vector representations, called Lane Graph Vectorization (LGV). In order to construct a driving policy for intersection navigation, we incorporate LGV into the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with Prioritized Experience Replay (PER). To train and validate the proposed algorithm, we construct a gym environment for intersection navigation within the high-fidelity CARLA simulator, integrating dense interactive traffic flow and various generalization test intersections with different maps. Experimental results demonstrate the effectiveness of LGV for intersection navigation tasks and outperform the state-of-the-art in our proposed scenarios. |
关键词 | Reinforcement Learning Autonomous Driving Intersection Navigating |
DOI | 10.1109/TCDS.2024.3384269 |
URL | 查看原文 |
收录类别 | SCI |
语种 | 英语 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 强化与进化学习 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57145 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
通讯作者 | Zhang, Qichao |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Coll Artificial Intelligence, Beijing 100049, Peoples R China 3.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 1000839, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Yuqi,Zhang, Qichao,Gao, Yinfeng,et al. Deep Reinforcement Learning-Based Driving Policy at Intersections Utilizing Lane Graph Networks[J]. IEEE Transactions on Cognitive and Developmental Systems,2024:1 - 16. |
APA | Liu, Yuqi,Zhang, Qichao,Gao, Yinfeng,&Zhao, Dongbin.(2024).Deep Reinforcement Learning-Based Driving Policy at Intersections Utilizing Lane Graph Networks.IEEE Transactions on Cognitive and Developmental Systems,1 - 16. |
MLA | Liu, Yuqi,et al."Deep Reinforcement Learning-Based Driving Policy at Intersections Utilizing Lane Graph Networks".IEEE Transactions on Cognitive and Developmental Systems (2024):1 - 16. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Deep_Reinforcement_L(22863KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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