Automatic Lane Change Maneuver in Dynamic Environment Using Model Predictive Control Method.pdf (289.26 kB)
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Automatic lane change maneuver in dynamic environment using model predictive control method

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conference contribution
posted on 05.03.2021, 12:08 by Zhaolun LiZhaolun Li, Jingjing JiangJingjing Jiang, Wen-Hua ChenWen-Hua Chen
The lane change maneuver is one of the typical maneuvers in various driving situations. Therefore the automatic lane change function is one of the key functions for autonomous vehicles. Many researches have been conducted in this field. Most existing work focused on the solutions for the static environment and assume that the surrounding vehicles are running at constant speeds. However, in reality, if not all the vehicles on the road are fully autonomous, the situation could be much more complicated and the ego vehicle has to deal with the dynamic environment. This paper proposes a Model Predictive Control (MPC)-based method to achieve automatic lane change in a dynamic environment. A two-wheel dynamic bicycle model, which combines the longitudinal and lateral motion of the ego vehicle, together with a utility function, which helps to automatically determine the target lane have been used in the algorithm. The simulation results have demonstrated the capability of the proposed algorithm in a dynamic environment.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Pages

2384-2389

Source

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

01/07/2020

Publication date

2021-02-10

Copyright date

2021

ISBN

9781728162126

ISSN

2153-0866

Location

Las Vegas, NV, USA

Event dates

25th October 2020 - 29th October 2020

Depositor

Mr Zhaolun Li . Deposit date: 5 August 2020

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