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Research on Lane-Change Decision and Planning in Multilane Expressway Scenarios for Autonomous Vehicles

Authors :
Chuanyin Tang
Lv Pan
Jifeng Xia
Shi Fan
Source :
Machines, Vol 11, Iss 8, p 820 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Taking into account the issues faced by self-driving vehicles in multilane expressway scenarios, a lane-change decision planning framework that considers two adjacent lanes is proposed. Based on this framework, the lateral stability of an autonomous vehicle under near-limit conditions during lane change is studied by the phase-plane method. Firstly, a state-machine-based driving logic is designed and a decision method is proposed to design the lane-change intention based on the surrounding traffic information and to consider the influence of the motion state of other vehicles in the adjacent lanes on the self-driving vehicle. In order to realize adaptive cruising under the full working conditions of the vehicle, a safety distance model is established for different driving speeds and switching strategies for fixed-speed cruising, following driving, and emergency braking are developed. Secondly, for the trajectory planning problem, a lane-change trajectory based on a quintuple polynomial optimization method is proposed. Then, the vehicle lateral stability boundary is investigated; the stability boundary and rollover boundary are incorporated into the designed path-tracking controller to improve the tracking accuracy while enhancing the rollover prevention capability. Finally, a simulation analysis is carried out through a joint simulation platform; the simulation results show that the proposed method can ensure the driving safety of autonomous vehicles in a multilane scenario.

Details

Language :
English
ISSN :
20751702
Volume :
11
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
edsdoj.91922fc8ebb64694a59a1b0a0b6e9a8c
Document Type :
article
Full Text :
https://doi.org/10.3390/machines11080820