Reference : Learn to Make Decision with Small Data for Autonomous Driving : Deep Gaussian Process...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/44375
Learn to Make Decision with Small Data for Autonomous Driving : Deep Gaussian Process and Feedback Control
English
Fang, Wenqi [> >]
Zhang, Shitian [> >]
Huang, Hui mailto [University of Luxembourg > >]
Dang, Shaobo [> >]
Huang, Zhejun [> >]
Li, Wenfei [> >]
Wang, Zheng [> >]
Sun, Tianfu [> >]
Li, Huiyun [> >]
28-Aug-2020
Journal of Advanced Transportation
Hindawi
2020
Yes (verified by ORBilu)
International
0197-6729
[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.
http://hdl.handle.net/10993/44375
10.1155/2020/8495264

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