Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon
This paper proposes an algorithm for path-following and collision avoidance of an autonomous vehicle based on model predictive control (MPC) using time-varying and non-uniformly spaced horizon. The MPC based on non-uniformly spaced horizon approach uses the time intervals that are small for the near...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9453818/ |
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| author | Minsung Kim Donggil Lee Joonwoo Ahn Minsoo Kim Jaeheung Park |
| author_facet | Minsung Kim Donggil Lee Joonwoo Ahn Minsoo Kim Jaeheung Park |
| author_sort | Minsung Kim |
| collection | DOAJ |
| description | This paper proposes an algorithm for path-following and collision avoidance of an autonomous vehicle based on model predictive control (MPC) using time-varying and non-uniformly spaced horizon. The MPC based on non-uniformly spaced horizon approach uses the time intervals that are small for the near future, and time intervals that are large for the distant future, to extend the length of the whole prediction horizon with a fixed number of prediction steps. This MPC has the advantage of being able to detect obstacles in advance because it can see the distant future. However, the presence of longer time interval samples may lead to poor path-following performance, especially for paths with high curvature. The proposed algorithm performs proper adjustment of the prediction interval according to a given situation. For sections with large curvature, it uses the short prediction intervals to increase the path-following performance; further, to consider obstacles over a wider range, it uses the long prediction intervals. This technique allows simultaneous improvement of the path-following performance and the range of obstacle avoidance with fixed computational complexity. The effectiveness of the proposed method is verified through an open-source simulator, CARLA and real-time experiments. |
| format | Article |
| id | doaj-art-a5d93433db7b4a619ed530b9d83d5321 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a5d93433db7b4a619ed530b9d83d53212025-08-25T23:05:58ZengIEEEIEEE Access2169-35362021-01-019864758648710.1109/ACCESS.2021.30889379453818Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced HorizonMinsung Kim0https://orcid.org/0000-0002-9146-7372Donggil Lee1Joonwoo Ahn2Minsoo Kim3https://orcid.org/0000-0003-2300-1279Jaeheung Park4https://orcid.org/0000-0002-5062-8264Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South KoreaDepartment of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul, South KoreaGraduate School of Convergence Science and Technology, Seoul National University, Seoul, South KoreaGraduate School of Convergence Science and Technology, Seoul National University, Seoul, South KoreaGraduate School of Convergence Science and Technology, Seoul National University, Seoul, South KoreaThis paper proposes an algorithm for path-following and collision avoidance of an autonomous vehicle based on model predictive control (MPC) using time-varying and non-uniformly spaced horizon. The MPC based on non-uniformly spaced horizon approach uses the time intervals that are small for the near future, and time intervals that are large for the distant future, to extend the length of the whole prediction horizon with a fixed number of prediction steps. This MPC has the advantage of being able to detect obstacles in advance because it can see the distant future. However, the presence of longer time interval samples may lead to poor path-following performance, especially for paths with high curvature. The proposed algorithm performs proper adjustment of the prediction interval according to a given situation. For sections with large curvature, it uses the short prediction intervals to increase the path-following performance; further, to consider obstacles over a wider range, it uses the long prediction intervals. This technique allows simultaneous improvement of the path-following performance and the range of obstacle avoidance with fixed computational complexity. The effectiveness of the proposed method is verified through an open-source simulator, CARLA and real-time experiments.https://ieeexplore.ieee.org/document/9453818/Path followingmodel predictive controlautonomous vehiclecollision avoidance |
| spellingShingle | Minsung Kim Donggil Lee Joonwoo Ahn Minsoo Kim Jaeheung Park Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon IEEE Access Path following model predictive control autonomous vehicle collision avoidance |
| title | Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon |
| title_full | Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon |
| title_fullStr | Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon |
| title_full_unstemmed | Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon |
| title_short | Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon |
| title_sort | model predictive control method for autonomous vehicles using time varying and non uniformly spaced horizon |
| topic | Path following model predictive control autonomous vehicle collision avoidance |
| url | https://ieeexplore.ieee.org/document/9453818/ |
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