English

WATonoBus: Field-Tested All-Weather Autonomous Shuttle Technology

Robotics 2024-10-28 v2 Artificial Intelligence Computer Vision and Pattern Recognition

Abstract

All-weather autonomous vehicle operation poses significant challenges, encompassing modules from perception and decision-making to path planning and control. The complexity arises from the need to address adverse weather conditions such as rain, snow, and fog across the autonomy stack. Conventional model-based single-module approaches often lack holistic integration with upstream or downstream tasks. We tackle this problem by proposing a multi-module and modular system architecture with considerations for adverse weather across the perception level, through features such as snow covered curb detection, to decision-making and safety monitoring. Through daily weekday service on the WATonoBus platform for almost two years, we demonstrate that our proposed approach is capable of addressing adverse weather conditions and provide valuable insights from edge cases observed during operation.

Keywords

Cite

@article{arxiv.2312.00938,
  title  = {WATonoBus: Field-Tested All-Weather Autonomous Shuttle Technology},
  author = {Neel P. Bhatt and Ruihe Zhang and Minghao Ning and Ahmad Reza Alghooneh and Joseph Sun and Pouya Panahandeh and Ehsan Mohammadbagher and Ted Ecclestone and Ben MacCallum and Ehsan Hashemi and Amir Khajepour},
  journal= {arXiv preprint arXiv:2312.00938},
  year   = {2024}
}

Comments

8 pages, 10 figures. This work has been submitted to the ITSC for possible publication

R2 v1 2026-06-28T13:38:53.989Z