Autonomous driving policy learning based on deep reinforcement learning and multi-type sensor data

被引：0
|
作者：
Yang S. [1 ]
Jiang Y.-D. [1 ]
Wu J. [1 ]
Liu H.-Z. [1 ]
机构：
[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2019年 / 49卷 / 04期
关键词：
Autonomous driving; Deep reinforcement learning; Lane keeping; Multi-type sensor data; Vehicle engineering;
D O I：
10.13229/j.cnki.jdxbgxb20180467
中图分类号：
学科分类号：
摘要：
This paper proposes a policy learning approach for autonomous driving based on the DRL and multi-type sensor data. Different Convolutional Neural networks (CNNs) are employed to deal with the data from different sources (i.e., high-dimensional data from camera and low-dimensional data from lidar, GPS, etc.). Then the extracted features from CNNs are combined for training the autonomous driving policy. Finally, the TORCS, which is an open-source simulation platform, is chosen to validate the proposed method. The results demonstrate that the multi-type sensor based DRL model can get good performance on velocity and lateral error control. © 2019, Jilin University Press. All right reserved.
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页码：1026 / 1033
页数：7
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