Research on trajectory planning and tracking control of autonomous delivery vehicles
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ID: 317247
2026
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Abstract
Abstract Trajectory planning and tracking control are critical components of autonomous driving and serve as a direct reflection of intelligent driving performance. This study focuses on low-speed autonomous delivery vehicles operating in closed-campus environments, investigating trajectory planning and tracking control algorithms tailored for such scenarios. A lattice-based path planning algorithm, founded on state-space sampling, is combined with a rule-based velocity planning method to achieve trajectory generation and obstacle avoidance. For vehicle control, a proportional-integral-derivative (PID) algorithm is implemented for longitudinal speed regulation, while a linear quadratic regulator (LQR)-based controller is designed for lateral path tracking. Finally, the proposed planning and control algorithms are deployed on an autonomous delivery vehicle for experimental validation, demonstrating effective trajectory planning and accurate tracking performance.The proposed framework is validated on a low-speed autonomous delivery vehicle platform, achieving smooth obstacle avoidance with a lateral acceleration below 0.5 m/s2 and a lateral tracking error of about 0.15 m under the optimal preview setting.
| Reference Key |
openalex_W7164675272
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| Authors | Chaobin Zhou, Kaiwei Zeng, Faan Wang, Jinhao Liang, Changfeng Shen, Jian Gong |
| Journal | Transportation Safety and Environment |
| Year | 2026 |
| DOI |
10.1093/tse/tdag019
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| URL | |
| Keywords | Keywords not found |
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