Vehicle sideslip trajectory prediction based on time-series analysis and multi-physical model fusion  

作　　者：Lipeng Cao Yugong Luo Yongsheng Wang Jian Chen Yansong He 

机构地区：[1]College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400030,China [2]School of Vehicle and Mobility,Tsinghua University,Beijing 100084,China

出　　处：《Journal of Intelligent and Connected Vehicles》2023年第3期161-172,共12页智能网联汽车（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.51975310).

摘　　要：On highways,vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles.To ensure their safety,predicting the sideslip trajectories of such vehicles is crucial.However,the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction.Hence,this study uses a physical model-based approach to predict vehicle sideslip trajectories.Nevertheless,the traditional physical model-based method relies on constant input assumption,making its long-term prediction accuracy poor.To address this challenge,this study presents the time-series analysis and interacting multiple model-based(IMM)sideslip trajectory prediction(TSIMMSTP)method,which encompasses time-series analysis and multi-physical model fusion,for the prediction of vehicle sideslip trajectories.Firstly,we use the proposed adaptive quadratic exponential smoothing method with damping(AQESD)in the time-series analysis module to predict the input state sequence required by kinematic models.Then,we employ an IMM approach to fuse the prediction results of various physical models.The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories.The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios,and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.

关 键 词：autonomous vehicle sideslip trajectory prediction adaptive quadratic exponential smoothing with damping(AQESD) interacting multiple model(IMM) 

分 类 号：U49[交通运输工程—交通运输规划与管理]