A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction

作     者：Giovanni Lucente Reza Dariani Julian Schindler Michael Ortgiese 

作者机构：Institute of Transporation SystemsGerman Aerospace Center(DLR)Lilienthalplatz 738108 BraunschweigGermany Fakultat Verkehrs‑und MaschinensystemeTU BerlinStrabe des 17.Juni 13510623 BerlinGermany 

出 版 物：《Automotive Innovation》 (汽车创新工程（英文）)

年 卷 期：2023年第6卷第3期

页      面：425-437页

核心收录：

学科分类：08[工学] 082304[工学-载运工具运用工程] 080204[工学-车辆工程] 0802[工学-机械工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：supported by the Institute of Transportation Systems of the German Aerospace Center (DLR) 

主　　题：Vehicle intention prediction Trajectory prediction Bayesian approach Mixed strategy Nash equilibrium 

摘      要：The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years,where connected and automated vehicles have to interact with human-driven *** this context,it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles,the possible maneuvers and the interactions between traffic participants within the seconds to *** article presents a Bayesian approach for vehicle intention forecasting,utilizing a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium(MSNE)as a prior estimate to model the reciprocal influence between traffic *** likelihood is then computed based on the Kullback-Leibler *** game is modeled as a static nonzero-sum polymatrix game with individual preferences,a well known strategic *** the MSNE for these games is in the PPAD∩PLS complexity class,with polynomial-time *** approach shows good results in simulations in the long term horizon(10s),with its computational complexity allowing for online applications.