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Author:

Hu, W. (Hu, W..) | Mu, H. (Mu, H..) | Chen, Y. (Chen, Y..) | Liu, Y. (Liu, Y..) | Li, X. (Li, X..)

Indexed by:

Scopus

Abstract:

This article focuses on the development of a stable pedestrian crash avoidance mitigation system for autonomous vehicles (AVs). Previous works have only used simple AV–pedestrian models, which do not reflect the actual interaction and risk status of intelligent intersections with manual vehicles. The paper presents a model that simulates the interaction between automatic driving vehicles and pedestrians on unsignalized crosswalks using the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The MADDPG algorithm optimizes the PCAM strategy through the continuous interaction of multiple independent agents and effectively captures the inherent uncertainty in continuous learning and human behavior. Experimental results show that the MADDPG model can fully mitigate collisions in different scenarios and outperforms the DDPG and DRL algorithms. © 2023 by the authors.

Keyword:

intersection risk autonomous–manual vehicle multi-agent driving behavior

Author Community:

  • [ 1 ] [Hu W.]School of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Hu W.]Research Institute for Road Safety of the Ministry of Public Security, Beijing, 100062, China
  • [ 3 ] [Mu H.]Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100085, China
  • [ 4 ] [Mu H.]School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100085, China
  • [ 5 ] [Chen Y.]School of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, China
  • [ 6 ] [Liu Y.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 7 ] [Li X.]Research Institute for Road Safety of the Ministry of Public Security, Beijing, 100062, China

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Source :

Sustainability (Switzerland)

ISSN: 2071-1050

Year: 2023

Issue: 7

Volume: 15

3 . 9 0 0

JCR@2022

ESI HC Threshold:17

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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