摘要
This study aims to propose a decision-making method based on artificial potential fields(APFs)and finite state machines(FSMs)in emergency conditions.This study presents a decision-making method based on APFs and FSMs for emergency conditions.By modeling the longitudinal and lateral potential energy fields of the vehicle,the driving state is identified,and the trigger conditions are provided for path planning during lane changing.In addition,this study also designed the state transition rules based on the longitudinal and lateral virtual forces.It established the vehicle decision-making model based on the finite state machine to ensure driving safety in emergency situations.To illustrate the performance of the decision-making model by considering APFs and finite state machines.The version of the model in the co-simulation platform of MATLAB and CarSim shows that the developed decision model in this study accurately generates driving behaviors of the vehicle at different time intervals.The contributions of this study are two-fold.A hierarchical vehicle state machine decision model is proposed to enhance driving safety in emergency scenarios.Mathematical models for determining the transition thresholds of lateral and longitudinal vehicle states are established based on the vehicle potential field model,leading to the formulation of transition rules between different states of autonomous vehicles(AVs).
基金
supported by the National Natural Science Foundation of China(Grant No.52102454)
the Postdoctoral Science Foundation of China(Grant No.2021M700169)
in part by the Natural Science Foundation of Chongqing(Grant No.cstc2021jcyj-msxmX0395)
the Special Funding for Postdoctoral Research Projects in Chongqing(Grant No.2021XM3069)
the Youth Project of Science and Technology Research Program of Chongqing Education Commission of China(Grant Nos.KJQN202001302 and KJQN202203909)
the Natural Science Foundation of Yongchuan District(Grant No.2023yc-jckx20089)
the Opening Project of Intelligent Policing Key Laboratory of Sichuan Province(Grant No.ZNJW2023KFQN002).
