Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment
Side collisions caused by sudden vehicle cut-ins comprise a significant proportion of traffic accidents. Due to the complex and dynamic nature of traffic environments, the warning algorithms in advanced driving assistant systems (ADAS) often misjudge and misdiagnose risk and omit necessary warnings,...
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| Published in: | IEEE transactions on intelligent transportation systems Vol. 23; no. 2; pp. 966 - 981 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1524-9050, 1558-0016 |
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| Abstract | Side collisions caused by sudden vehicle cut-ins comprise a significant proportion of traffic accidents. Due to the complex and dynamic nature of traffic environments, the warning algorithms in advanced driving assistant systems (ADAS) often misjudge and misdiagnose risk and omit necessary warnings, because they rely solely on the sensing information of the single vehicle equipped with ADAS and have limited insights from and communication with the surrounding vehicles and traffic environment. To improve the effectiveness of ADAS in cut-in scenarios, this study established a collision warning model in a vehicle-to-vehicle (V2V) communication environment. Firstly, based on the support vector machine-recursive feature elimination (SVM-RFE) lane-change intent-recognition model, the lane-change feasibility and the change rate of the lateral offset, the logical "and" was used to establish a lane-change behavior prediction model, and a trajectory prediction model was established based on the long short-term memory (LSTM). Then, based on the proposed comprehensive prediction model for lane-change behavior, the driving trajectory prediction model, and the oriented bounding box (OBB) detection algorithm, a collision warning model was established for a V2V environment. Finally, based on a driving simulation platform and a real-world vehicle test, a cut-in experiment in a V2V environment was designed and implemented. By comparing the warning confusion matrix and warning time, it was found that the proposed cut-in collision warning model is superior to the traditional collision warning model. The results of this study can provide new modeling ideas and a theoretical basis for ADAS to further optimize for a cut-in scenario. |
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| AbstractList | Side collisions caused by sudden vehicle cut-ins comprise a significant proportion of traffic accidents. Due to the complex and dynamic nature of traffic environments, the warning algorithms in advanced driving assistant systems (ADAS) often misjudge and misdiagnose risk and omit necessary warnings, because they rely solely on the sensing information of the single vehicle equipped with ADAS and have limited insights from and communication with the surrounding vehicles and traffic environment. To improve the effectiveness of ADAS in cut-in scenarios, this study established a collision warning model in a vehicle-to-vehicle (V2V) communication environment. Firstly, based on the support vector machine-recursive feature elimination (SVM-RFE) lane-change intent-recognition model, the lane-change feasibility and the change rate of the lateral offset, the logical “and” was used to establish a lane-change behavior prediction model, and a trajectory prediction model was established based on the long short-term memory (LSTM). Then, based on the proposed comprehensive prediction model for lane-change behavior, the driving trajectory prediction model, and the oriented bounding box (OBB) detection algorithm, a collision warning model was established for a V2V environment. Finally, based on a driving simulation platform and a real-world vehicle test, a cut-in experiment in a V2V environment was designed and implemented. By comparing the warning confusion matrix and warning time, it was found that the proposed cut-in collision warning model is superior to the traditional collision warning model. The results of this study can provide new modeling ideas and a theoretical basis for ADAS to further optimize for a cut-in scenario. |
| Author | Wen, Jiaqiang Wu, Chaozhong Lyu, Nengchao Duan, Zhicheng |
| Author_xml | – sequence: 1 givenname: Nengchao orcidid: 0000-0002-0926-9140 surname: Lyu fullname: Lyu, Nengchao email: lnc@whut.edu.cn organization: Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China – sequence: 2 givenname: Jiaqiang surname: Wen fullname: Wen, Jiaqiang organization: Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China – sequence: 3 givenname: Zhicheng surname: Duan fullname: Duan, Zhicheng organization: Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China – sequence: 4 givenname: Chaozhong surname: Wu fullname: Wu, Chaozhong organization: Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China |
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| Snippet | Side collisions caused by sudden vehicle cut-ins comprise a significant proportion of traffic accidents. Due to the complex and dynamic nature of traffic... |
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| SubjectTerms | Accidents Advanced driver assistance systems Alarm systems Algorithms Collision avoidance cut-in collisions Lane changing long-term and short-term memory networks Prediction models Predictive models Safety support vector machine Support vector machines Traffic accidents Trajectory Vehicle dynamics vehicle-to-vehicle communication Vehicles Warning |
| Title | Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment |
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