公开(公告)号：US11964655B2

公开(公告)日：2024-04-23

申请号：US17766870

申请日：2021-04-12

Applicant: SOUTHEAST UNIVERSITY

Inventor： Xu Li ,  Weiming Hu ,  Jinchao Hu ,  Xuefen Zhu

IPC: B60W30/095 ,  G06N3/045 ,  G06N3/0464

CPC classification number: B60W30/0956 ,  G06N3/045 ,  G06N3/0464 ,  B60W2300/125 ,  B60W2420/408 ,  B60W2554/4041 ,  B60W2554/80

Abstract: The present invention discloses a backward anti-collision driving decision-making method for a heavy commercial vehicle. Firstly, a traffic environment model is established, and movement state information of a heavy commercial vehicle and a vehicle behind the heavy commercial vehicle is collected. Secondly, a backward collision risk assessment model based on backward distance collision time is established, and a backward collision risk is accurately quantified. Finally, a backward anti-collision driving decision-making problem is described as a Markov decision-making process under a certain reward function, a backward anti-collision driving decision-making model based on deep reinforcement learning is established, and an effective, reliable and adaptive backward anti-collision driving decision-making policy is obtained. The method provided by the present invention can overcome the defect of lack for research on the backward anti-collision driving decision-making policy for the heavy commercial vehicle in the existing method, can quantitatively output proper steering wheel angle and throttle opening control quantities, can provide effective and reliable backward anti-collision driving suggestions for a driver, and can reduce backward collision accidents.