Model predictive path planning based on artificial potential field and its application to autonomous lane change
- Authors
- Lin; P.; Choi; W.Y.; Lee, Seung-hi; S.-H.; Chung; C.C.
- Issue Date
- 2020
- Publisher
- IEEE Computer Society
- Keywords
- Artificial Potential Field; Autonomous Vehicle; Collision Avoidance; Lane Change; Optimal Path Planning
- Citation
- International Conference on Control, Automation and Systems, v.2020-October, pp.731 - 736
- Journal Title
- International Conference on Control, Automation and Systems
- Volume
- 2020-October
- Start Page
- 731
- End Page
- 736
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12533
- DOI
- 10.23919/ICCAS50221.2020.9268380
- ISSN
- 1598-7833
- Abstract
- In this paper, we propose a vehicle lane change system using model predictive path planning (MPPP) based on the artificial potential field (APF) for speeding vehicles. It is shown that APF has high performance in real-time obstacle avoidance. However, it remains unpractical for self-driving cars because the point model used for the APF ignores the lateral vehicle dynamics for the lane-keeping system. To resolve the problem, this paper introduces a novel curve-fitting method combined with the APF applied to plan a drivable path for autonomous vehicles in the lane change action. The proposed system was validated through MATLAB/Simulink with the empirical kinematic model. The simulation results indicate that the model predictive path planning algorithm is highly effective in high-speed lane change scenarios to avoid dynamic obstacle vehicles. © 2020 Institute of Control, Robotics, and Systems - ICROS.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > Department of Mechanical and System Design Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.