Master general parking skill via deep learning
Lin YL(林懿伦)1,3; Li L(李力)2; Dai XY(戴星原)1,3; Zheng NN(郑南宁)4; Wang FY(王飞跃)1; Li L(李力)
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
DOI10.1109/IVS.2017.7995836
收录类别EI
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
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
通讯作者Li L(李力)
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
推荐引用方式
GB/T 7714
Lin YL,Li L,Dai XY,et al. Master general parking skill via deep learning[C],2017.
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