Knowledge Commons of Institute of Automation,CAS
Benchmarking lane-changing decision-making for deep reinforcement learning | |
Wang JJ(王俊杰)1,2; Zhang QC(张启超)1,2; Zhao DB(赵冬斌)1,2 | |
2021-10 | |
会议名称 | International Conference on Robotics and Artificial Intelligence |
会议日期 | 2021-11 |
会议地点 | Guangzhou, China |
摘要 | The development of autonomous driving has attracted extensive attention in recent years, and it is essential to evaluate the performance of autonomous driving. However, testing on the road is expensive and inefficient. Virtual testing is the primary way to validate and verify self-driving cars, and the basis of virtual testing is to build simulation scenarios. In this paper, we propose a training, testing, and evaluation pipeline for the lane-changing task from the perspective of deep reinforcement learning. First, we design lane change scenarios for training and testing, where the test scenarios include stochastic and deterministic parts. Then, we deploy a set of benchmarks consisting of learning and non-learning approaches. We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios. The designed lane-changing scenarios and benchmarks are both opened to provide a consistent experimental environment for the lane-changing task. |
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 | 智能能力评估 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51722 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang JJ,Zhang QC,Zhao DB. Benchmarking lane-changing decision-making for deep reinforcement learning[C],2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Benchmarking Lane-ch(1117KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论