Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control
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 reinf...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8495264 |
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| _version_ | 1850109515601543168 |
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| author | Wenqi Fang Shitian Zhang Hui Huang Shaobo Dang Zhejun Huang Wenfei Li Zheng Wang Tianfu Sun Huiyun Li |
| author_facet | Wenqi Fang Shitian Zhang Hui Huang Shaobo Dang Zhejun Huang Wenfei Li Zheng Wang Tianfu Sun Huiyun Li |
| author_sort | Wenqi Fang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4ab5c802bf4748e1b772fc7189250dfe |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-4ab5c802bf4748e1b772fc7189250dfe2025-08-20T02:38:03ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/84952648495264Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback ControlWenqi Fang0Shitian Zhang1Hui Huang2Shaobo Dang3Zhejun Huang4Wenfei Li5Zheng Wang6Tianfu Sun7Huiyun Li8Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, ChinaAutonomous 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://dx.doi.org/10.1155/2020/8495264 |
| spellingShingle | Wenqi Fang Shitian Zhang Hui Huang Shaobo Dang Zhejun Huang Wenfei Li Zheng Wang Tianfu Sun Huiyun Li Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control Journal of Advanced Transportation |
| title | Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control |
| title_full | Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control |
| title_fullStr | Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control |
| title_full_unstemmed | Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control |
| title_short | Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control |
| title_sort | learn to make decision with small data for autonomous driving deep gaussian process and feedback control |
| url | http://dx.doi.org/10.1155/2020/8495264 |
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