Master general parking skill via deep learning
Yiun Lin1,3; Li Li2; Xingyuan Dai1,3; Zheng Nanning4; Wang Fei-Yue1
Conference Name2017 IEEE Intelligent Vehicles Symposium (IV)
Source PublicationIEEE Intelligent Vehicles Symposium, Proceedings
Conference Date2017
Conference PlaceLos Angeles, CA, USA

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.

Keyword智能汽车 轨迹规划
Subject Area智能汽车
Indexed ByEI
Citation statistics
Document Type会议论文
Corresponding AuthorLi Li
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yiun Lin,Li Li,Xingyuan Dai,et al. Master general parking skill via deep learning[C],2017.
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