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Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

文献类型：期刊论文

作者Huang, Rulin1,2; Liang, Huawei2; Zhao, Pan2; Yu, Biao2; Geng, Xinli1,2
刊名APPLIED SCIENCES-BASEL
出版日期2017-05-01
卷号7期号:5页码:1-21
关键词Intent Estimation Motion Model Autonomous Vehicle Collision Avoidance Trajectory Prediction
DOI10.3390/app7050457
文献子类Article
英文摘要Existing collision avoidance methods for autonomous vehicles, which ignore the driving intent of detected vehicles, thus, cannot satisfy the requirements for autonomous driving in urban environments because of their high false detection rates of collisions with vehicles on winding roads and the missed detection rate of collisions with maneuvering vehicles. This study introduces an intent-estimation-and motion-model-based (IEMMB) method to address these disadvantages. First, a state vector is constructed by combining the road structure and the moving state of detected vehicles. A Gaussian mixture model is used to learn the maneuvering patterns of vehicles from collected data, and the patterns are used to estimate the driving intent of the detected vehicles. Then, a desirable long-term trajectory is obtained by weighting time and comfort. The long-term trajectory and the short-term trajectory, which are predicted using a constant yaw rate motion model, are fused to achieve an accurate trajectory. Finally, considering the moving state of the autonomous vehicle, collisions can be detected and avoided. Experiments have shown that the intent estimation method performed well, achieving an accuracy of 91.7% on straight roads and an accuracy of 90.5% on winding roads, which is much higher than that achieved by the method that ignores the road structure. The average collision detection distance is increased by more than 8 m. In addition, the maximum yaw rate and acceleration during an evasive maneuver are decreased, indicating an improvement in the driving comfort.
WOS关键词CHALLENGE
WOS研究方向Chemistry ; Materials Science ; Physics
语种英语
WOS记录号WOS:000404449000025
资助机构National Nature Science Foundations of China(61503362 ; National Nature Science Foundations of China(61503362 ; National Nature Science Foundations of China(61503362 ; National Nature Science Foundations of China(61503362 ; National Nature Science Foundations of Anhui Province(1508085MF133) ; National Nature Science Foundations of Anhui Province(1508085MF133) ; National Nature Science Foundations of Anhui Province(1508085MF133) ; National Nature Science Foundations of Anhui Province(1508085MF133) ; Guangdong Province Science and Technology Plan(2016B090910002) ; Guangdong Province Science and Technology Plan(2016B090910002) ; Guangdong Province Science and Technology Plan(2016B090910002) ; Guangdong Province Science and Technology Plan(2016B090910002) ; 61305111 ; 61305111 ; 61305111 ; 61305111 ; 61304100 ; 61304100 ; 61304100 ; 61304100 ; 91420104) ; 91420104) ; 91420104) ; 91420104) ; National Nature Science Foundations of China(61503362 ; National Nature Science Foundations of China(61503362 ; National Nature Science Foundations of China(61503362 ; National Nature Science Foundations of China(61503362 ; National Nature Science Foundations of Anhui Province(1508085MF133) ; National Nature Science Foundations of Anhui Province(1508085MF133) ; National Nature Science Foundations of Anhui Province(1508085MF133) ; National Nature Science Foundations of Anhui Province(1508085MF133) ; Guangdong Province Science and Technology Plan(2016B090910002) ; Guangdong Province Science and Technology Plan(2016B090910002) ; Guangdong Province Science and Technology Plan(2016B090910002) ; Guangdong Province Science and Technology Plan(2016B090910002) ; 61305111 ; 61305111 ; 61305111 ; 61305111 ; 61304100 ; 61304100 ; 61304100 ; 61304100 ; 91420104) ; 91420104) ; 91420104) ; 91420104)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/31968]  
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 230026, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Rulin,Liang, Huawei,Zhao, Pan,et al. Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments[J]. APPLIED SCIENCES-BASEL,2017,7(5):1-21.
APA Huang, Rulin,Liang, Huawei,Zhao, Pan,Yu, Biao,&Geng, Xinli.(2017).Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments.APPLIED SCIENCES-BASEL,7(5),1-21.
MLA Huang, Rulin,et al."Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments".APPLIED SCIENCES-BASEL 7.5(2017):1-21.

入库方式： OAI收割

来源：合肥物质科学研究院

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