Model Predictive Path Planning Based on Artificial Potential Field and Its Application to Autonomous Lane Change
- Authors
- Lin, Pengfei; Choi, Woo Young; Lee, Seung-Hi; Chung, Chung Choo
- Issue Date
- 2020
- Publisher
- IEEE
- Keywords
- Autonomous Vehicle; Collision Avoidance; Artificial Potential Field; Lane Change; Optimal Path Planning
- Citation
- 2020 20TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), pp.731 - 736
- Journal Title
- 2020 20TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS)
- Start Page
- 731
- End Page
- 736
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/27984
- ISSN
- 2093-7121
- 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.
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Collections - College of Engineering > Department of Mechanical and System Design Engineering > 1. Journal Articles
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