首页|Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
Accurately predicting the trajectories of surround-ing vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study pro-poses a cut-in prediction and risk assessment method with con-sidering the interactions of multiple traffic participants.The inte-gration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training effi-ciency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax func-tion.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higherprecisionandeffectively evaluate therisklevelofacut-in mane-uver compared to the methods without considering interaction.

Cut-in behaviorinteraction-awaremixed trafficrisk assessmenttrajectory prediction

Xianglei Zhu、Wen Hu、Zejian Deng、Jinwei Zhang、Fengqing Hu、Rui Zhou、Keqiu Li、Fei-Yue Wang

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College of Intelligence and Computing,Tianjin University,Tianjin 300350,and also with the China Automotive Technology and Research Center Co.Ltd.,Tianjin 300300,China

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China

Department of Mechanical and Mechatronics Engineering,University of Waterloo,Waterloo,ON N2L3G1,Canada

School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China

Macau University of Science and Technology,Macau,China,and also with the Waytous Inc.,Qingdao 266000,China

College of Intelligence and Computing,Tianjin University,Tianjin 300300,China

State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

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2020B090905000320204ABC03A13

2022

10.1109/JAS.2022.105866

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDCSCDSCIEI
ISSN：2329-9266
年,卷(期)：2022.9(10)
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