Model Predictive Path Planning Based on Artificial Potential Field and Its Application to Autonomous Lane Change
- Title
- Model Predictive Path Planning Based on Artificial Potential Field and Its Application to Autonomous Lane Change
- Author
- 정정주
- Keywords
- Autonomous Vehicle; Collision Avoidance; Artificial Potential Field; Lane Change; Optimal Path Planning
- Issue Date
- 2020-10
- Publisher
- ICROS
- Citation
- 2020 20th International Conference on Control, Automation and Systems (ICCAS 2020), page.731-736
- 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.
- URI
- https://ieeexplore.ieee.org/document/9268380https://repository.hanyang.ac.kr/handle/20.500.11754/171827
- ISSN
- 2642-3901; 1598-7833
- DOI
- 10.23919/ICCAS50221.2020.9268380
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
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