Article (Scientific journals)
Learn to Make Decision with Small Data for Autonomous Driving : Deep Gaussian Process and Feedback Control
Fang, Wenqi; Zhang, Shitian; Huang, Hui et al.
2020In Journal of Advanced Transportation, 2020
Peer reviewed
 

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Abstract :
[en] Autonomous driving is a popular and promising field in artificial intelligence. Rapid decision of the next action according to the latest few actions and status, such as acceleration, brake, and steering angle, is a major concern for autonomous driving. There are some learning methods, such as reinforcement learning which automatically learns the decision. However, it usually requires large volume of samples. In this paper, to reduce the sample size, we exploit the deep Gaussian process, where a regression model is trained on small sample datasets and captures the most significant features correctly. Besides, to realize the real-time and close-loop control, we combine the feedback control into the process. Experimental results on the Torcs simulation engine illustrate smooth driving on virtual road which can be achieved. Compared with the amount of training data in deep reinforcement learning, our method uses only 0.34\% of its size and obtains similar simulation results. It may be useful for real road tests in the future.
Disciplines :
Computer science
Author, co-author :
Fang, Wenqi
Zhang, Shitian
Huang, Hui ;  University of Luxembourg
Dang, Shaobo
Huang, Zhejun
Li, Wenfei
Wang, Zheng
Sun, Tianfu
Li, Huiyun
External co-authors :
yes
Language :
English
Title :
Learn to Make Decision with Small Data for Autonomous Driving : Deep Gaussian Process and Feedback Control
Publication date :
28 August 2020
Journal title :
Journal of Advanced Transportation
ISSN :
0197-6729
Publisher :
Hindawi
Volume :
2020
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 28 September 2020

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