Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
173 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Enhancing Campus Mobility: Achievements and Challenges of Autonomous Shuttle "Snow Lion'' (2401.08939v1)

Published 17 Jan 2024 in cs.RO

Abstract: The rapid evolution of autonomous vehicles (AVs) has significantly influenced global transportation systems. In this context, we present ``Snow Lion'', an autonomous shuttle meticulously designed to revolutionize on-campus transportation, offering a safer and more efficient mobility solution for students, faculty, and visitors. The primary objective of this research is to enhance campus mobility by providing a reliable, efficient, and eco-friendly transportation solution that seamlessly integrates with existing infrastructure and meets the diverse needs of a university setting. To achieve this goal, we delve into the intricacies of the system design, encompassing sensing, perception, localization, planning, and control aspects. We evaluate the autonomous shuttle's performance in real-world scenarios, involving a 1146-kilometer road haul and the transportation of 442 passengers over a two-month period. These experiments demonstrate the effectiveness of our system and offer valuable insights into the intricate process of integrating an autonomous vehicle within campus shuttle operations. Furthermore, a thorough analysis of the lessons derived from this experience furnishes a valuable real-world case study, accompanied by recommendations for future research and development in the field of autonomous driving.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (7)
  1. T. Liu, Q. hai Liao, L. Gan, F. Ma, J. Cheng, X. Xie, Z. Wang, Y. Chen, Y. Zhu, S. Zhang et al., “The role of the hercules autonomous vehicle during the covid-19 pandemic: An autonomous logistic vehicle for contactless goods transportation,” IEEE Robotics & Automation Magazine, vol. 28, no. 1, pp. 48–58, 2021.
  2. Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4490–4499.
  3. J. Jiao, Q. Liao, Y. Zhu, T. Liu, Y. Yu, R. Fan, L. Wang, and M. Liu, “A novel dual-lidar calibration algorithm using planar surfaces,” in 2019 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2019, pp. 1499–1504.
  4. T. Shan and B. Englot, “Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 4758–4765.
  5. M. I. Valls, H. F. Hendrikx, V. J. Reijgwart, F. V. Meier, I. Sa, R. Dubé, A. Gawel, M. Bürki, and R. Siegwart, “Design of an autonomous racecar: Perception, state estimation and system integration,” in 2018 IEEE international conference on robotics and automation (ICRA).   IEEE, 2018, pp. 2048–2055.
  6. Y. Chen, R. Xin, J. Cheng, Q. Zhang, X. Mei, M. Liu, and L. Wang, “Efficient speed planning for autonomous driving in dynamic environment with interaction point model,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11 839–11 846, 2022.
  7. J. Cheng, Y. Chen, Q. Zhang, L. Gan, C. Liu, and M. Liu, “Real-time trajectory planning for autonomous driving with gaussian process and incremental refinement,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 8999–9005.

Summary

We haven't generated a summary for this paper yet.