GRAPH-CONSTRAINED DIFFUSION FOR END-TO-END PATH PLANNING

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Author(s)

  • Dingyuan Shi
  • Yongxin Tong
  • Ke Xu
  • Zheng Wang
  • Jieping Ye

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication12th International Conference on Learning Representations, ICLR 2024
PublisherInternational Conference on Learning Representations, ICLR
Number of pages19
Publication statusPublished - May 2024

Publication series

NameInternational Conference on Learning Representations, ICLR

Conference

Title12th International Conference on Learning Representations (ICLR 2024)
LocationMesse Wien Exhibition and Congress Center
PlaceAustria
CityVienna
Period7 - 11 May 2024

Abstract

Path planning underpins various applications such as transportation, logistics, and robotics. Conventionally, path planning is formulated with explicit optimization objectives such as distance or time. However, real-world data reveals that user intentions are hard-to-model, suggesting a need for data-driven path planning that implicitly incorporates the complex user intentions. In this paper, we propose GDP, a diffusion-based model for end-to-end data-driven path planning. It effectively learns path patterns via a novel diffusion process that incorporates constraints from road networks, and plans paths as conditional path generation given the origin and destination as prior evidence. GDP is the first solution that bypasses the traditional search-based frameworks, a long-standing performance bottleneck in path planning. We validate the efficacy of GDP on two real-world datasets. Our GDP beats strong baselines by 14.2% ∼ 43.5% and achieves state-of-the-art performances. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

Citation Format(s)

GRAPH-CONSTRAINED DIFFUSION FOR END-TO-END PATH PLANNING. / Shi, Dingyuan; Tong, Yongxin; Zhou, Zimu et al.
12th International Conference on Learning Representations, ICLR 2024. International Conference on Learning Representations, ICLR, 2024. (International Conference on Learning Representations, ICLR).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review