Knowledge Commons of Institute of Automation,CAS
Master general parking skill via deep learning | |
Yiun Lin1,3; Li Li2; Xingyuan Dai1,3; Zheng Nanning4; Wang Fei-Yue1 | |
2017 | |
会议名称 | 2017 IEEE Intelligent Vehicles Symposium (IV) |
会议录名称 | IEEE Intelligent Vehicles Symposium, Proceedings |
会议日期 | 2017 |
会议地点 | Los Angeles, CA, USA |
摘要 | Parking is one basic function of autonomous vehicles. However, parking still remains difficult to be implemented, since it requires to generate a relatively long-term series of actions to reach a certain objective under complicated constraints. One recently proposed method used deep neural networks(DNN) to learn the relationship between the actual parking trajectories and the corresponding steering actions, so as to find the best parking trajectory via direct recalling. However, this method can only handle a special vehicle whose dynamic parameters are well known. In this paper, we use transfer learning technique to further extend this direct trajectory planning method and master general parking skills. We aim to mimic how human drivers make parking by using a specially designed deep neural network. The first few layers of this DNN contain the general parking trajectory planning knowledge for all kinds of vehicles; while the last few layers of this DNN can be quickly tuned to adapt various kinds of vehicles. Numerical tests show that, combining transfer learning and direct trajectory planning solution, our new approach enables automated vehicles to convey the knowledge of trajectory planning from one vehicle to another with a few try-and-tests. |
关键词 | 智能汽车 轨迹规划 |
学科领域 | 智能汽车 |
DOI | 10.1109/IVS.2017.7995836 |
收录类别 | EI |
语种 | 英语 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20242 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 复杂系统管理与控制国家重点实验室 |
通讯作者 | Li Li |
作者单位 | 1.中国科学院自动化研究所 先进控制与自动化团队 2.清华大学 自动化系 3.中国科学院大学 4.西安交通大学 |
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 | Yiun Lin,Li Li,Xingyuan Dai,et al. Master general parking skill via deep learning[C],2017. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
07995836.pdf(826KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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