@inproceedings{e8323b50c73b406a99746c13cb7535e0,
title = "A Motion Planning Framework with Learning Based Trajectory Prediction in Self Driving",
abstract = "Efficient and reliable motion planning system is critical in changing environments for autonomous driving. In this paper, we present a motion planning algorithm for dynamic scenarios through Gaussian process(GP) path planner and trajectory predictor. Firstly we plan a feasible path with GP planner. Then, the predictor generates several possible trajectories of other participants and we use a S-T graph speed planner to produce the speed profile with predicted results. Finally, simulation results demonstrate that our algorithm can improve the success rate of random driving tasks compared to the commonly used constant velocity assumption.",
keywords = "Gaussian process, Motion planning, Trajectory prediction",
author = "Feiyu Bian and Xing Liu and Yizhai Zhang and Zhiqiang Ma and Ganghui Shen and Panfeng Huang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2200-9_19",
language = "英语",
isbn = "9789819621996",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "191--201",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 1",
}