A Motion Planning Framework with Learning Based Trajectory Prediction in Self Driving

Feiyu Bian, Xing Liu, Yizhai Zhang, Zhiqiang Ma, Ganghui Shen, Panfeng Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationAdvances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 1
EditorsLiang Yan, Haibin Duan, Yimin Deng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages191-201
Number of pages11
ISBN (Print)9789819621996
DOIs
StatePublished - 2025
EventInternational Conference on Guidance, Navigation and Control, ICGNC 2024 - Changsha, China
Duration: 9 Aug 202411 Aug 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1337 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Guidance, Navigation and Control, ICGNC 2024
Country/TerritoryChina
CityChangsha
Period9/08/2411/08/24

Keywords

  • Gaussian process
  • Motion planning
  • Trajectory prediction

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