Human-like lane-change decision making for automated driving with a game theoretic approach

Peng Hang, Chen Lv, Chao Huang, Yang Xing, Zhongxu Hu, Jiacheng Cai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

With the consideration of personalized driving for automated vehicles (AVs), this paper presents a human-like decision making framework for AVs. In the modelling process, the driver model is combined with the vehicle model, which yields the integrated model for the decision-making algorithm design. Three different driving styles, i.e., aggressive, normal, and conservative, are defined for human-like driving modelling. Additionally, motion prediction algorithm is designed with model predictive control (MPC) to advance the effectiveness of the decision-making approach. Furthermore, the decision-making cost function is constructed considering drive safety, ride comfort and travel efficiency, which reflect different driving styles. Based on the decision-making cost function, a noncooperative game theoretic approach is applied to solving the decision-making issue. Finally, the proposed human-like decision making algorithm is evaluated with an overtaking scenario. Testing results indicate different driving styles cause different decision-making results, and the designed algorithm can always make safe and reasonable decisions for AVs.

Original languageEnglish
Title of host publication2020 4th CAA International Conference on Vehicular Control and Intelligence, CVCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages708-713
Number of pages6
ISBN (Electronic)9781728184968
DOIs
Publication statusPublished - 18 Dec 2020
Externally publishedYes
Event4th CAA International Conference on Vehicular Control and Intelligence, CVCI 2020 - Hangzhou, China
Duration: 18 Dec 202020 Dec 2020

Publication series

Name2020 4th CAA International Conference on Vehicular Control and Intelligence, CVCI 2020

Conference

Conference4th CAA International Conference on Vehicular Control and Intelligence, CVCI 2020
Country/TerritoryChina
CityHangzhou
Period18/12/2020/12/20

Keywords

  • Automated vehicle
  • Decision making
  • Game theoretic approach
  • Human-like
  • Lane change

ASJC Scopus subject areas

  • Artificial Intelligence
  • Automotive Engineering
  • Control and Optimization

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