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	A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
	Geng, Xinli1,2; Liang, Huawei2; Yu, Biao2; Zhao, Pan2; He, Liuwei1,2; Huang, Rulin1,2
刊名	APPLIED SCIENCES-BASEL
	2017-04-01
卷号	7期号:4
关键词	Autonomous Vehicle Scenario-adaptive Driving Behavior Prediction Ontology Model
DOI	10.3390/app7040426
文献子类	Article
英文摘要	Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles' driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario's characteristics. Finally, the target vehicle's future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%.
WOS关键词	MODELS ; FRAMEWORK
WOS研究方向	Chemistry ; Materials Science ; Physics
语种	英语
WOS记录号	WOS:000404447600115
资助机构	Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Institute of Applied Technology, Hefei Institute of Physical Science ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; Academy of Sciences of China ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; National Natural Science Foundation of China(61503362 ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; Natural Science Foundation of Anhui Province(1508085MF133) ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 91420104 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 51405471 ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; 61503363) ; 61503363)
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/33446]
专题	合肥物质科学研究院_应用技术研究所
作者单位	1.Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Appl Technol, Hefei 230008, Peoples R China
推荐引用方式 GB/T 7714	Geng, Xinli,Liang, Huawei,Yu, Biao,et al. A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving[J]. APPLIED SCIENCES-BASEL,2017,7(4).
APA	Geng, Xinli,Liang, Huawei,Yu, Biao,Zhao, Pan,He, Liuwei,&Huang, Rulin.(2017).A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving.APPLIED SCIENCES-BASEL,7(4).
MLA	Geng, Xinli,et al."A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving".APPLIED SCIENCES-BASEL 7.4(2017).

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