Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
8 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

Results and Lessons Learned from Autonomous Driving Transportation Services in Airfield, Crowded Indoor, and Urban Environments (2403.01233v2)

Published 2 Mar 2024 in cs.RO

Abstract: Autonomous vehicles have been actively investigated over the past few decades. Several recent works show the potential of autonomous vehicles in urban environments with impressive experimental results. However, these works note that autonomous vehicles are still occasionally inferior to expert drivers in complex scenarios. Furthermore, they do not focus on the possibilities of autonomous driving transportation services in other areas beyond urban environments. This paper presents the research results and lessons learned from autonomous driving transportation services in airfield, crowded indoor, and urban environments. We discuss how we address several unique challenges in these diverse environments. We also offer an overview of remaining challenges that have not received much attention but must be addressed. This paper aims to share our unique experience to support researchers who are interested in exploring autonomous driving transportation services in various real-world environments.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. J. Borrego-Carazo, D. Castells-Rufas, E. Biempica, and J. Carrabina, “Resource-constrained machine learning for adas: A systematic review,” IEEE Access, vol. 8, pp. 40 573–40 598, 2020.
  2. X. Li, K.-Y. Lin, M. Meng, X. Li, L. Li, Y. Hong, and J. Chen, “A survey of adas perceptions with development in china,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 14 188–14 203, 2022.
  3. Z. Yang, Y. Chai, D. Anguelov, Y. Zhou, P. Sun, D. Erhan, S. Rafferty, and H. Kretzschmar, “Surfelgan: Synthesizing realistic sensor data for autonomous driving,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11 115–11 124.
  4. J. Nidamanuri, C. Nibhanupudi, R. Assfalg, and H. Venkataraman, “A progressive review: Emerging technologies for adas driven solutions,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 2, pp. 326–341, 2022.
  5. R. Morris, M. L. Chang, R. Archer, E. V. Cross, S. Thompson, J. Franke, R. Garrett, W. Malik, K. McGuire, and G. Hemann, “Self-driving aircraft towing vehicles: A preliminary report,” in Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
  6. Y. Morales, N. Kallakuri, K. Shinozawa, T. Miyashita, and N. Hagita, “Human-comfortable navigation for an autonomous robotic wheelchair,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 2737–2743.
  7. J. Leaman and H. M. La, “A comprehensive review of smart wheelchairs: Past, present, and future,” IEEE Transactions on Human-Machine Systems, vol. 47, no. 4, pp. 486–499, 2017.
  8. T. Nothdurft, P. Hecker, S. Ohl, F. Saust, M. Maurer, A. Reschka, and J. R. Böhmer, “Stadtpilot: First fully autonomous test drives in urban traffic,” in 2011 14th international IEEE conference on intelligent transportation systems (ITSC).   IEEE, 2011, pp. 919–924.
  9. A. Broggi, P. Cerri, S. Debattisti, M. C. Laghi, P. Medici, D. Molinari, M. Panciroli, and A. Prioletti, “Proud—public road urban driverless-car test,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp. 3508–3519, 2015.
  10. J. Ziegler, P. Bender, M. Schreiber, H. Lategahn, T. Strauss, C. Stiller, T. Dang, U. Franke, N. Appenrodt, C. G. Keller et al., “Making bertha drive—an autonomous journey on a historic route,” IEEE Intelligent Transportation Systems Magazine, vol. 6, no. 2, pp. 8–20, 2014.
  11. S. Wang, Y. Che, H. Zhao, and A. Lim, “Accurate tracking, collision detection, and optimal scheduling of airport ground support equipment,” IEEE Internet of Things Journal, vol. 8, no. 1, pp. 572–584, 2021.
  12. J. Lee, H. Lee, and J. Oh, “Fusionloc: Camera-2d lidar fusion using multi-head self-attention for end-to-end serving robot relocalization,” arXiv preprint arXiv:2303.06872, 2023.
  13. E. Tsiogas, I. Kleitsiotis, I. Kostavelis, A. Kargakos, D. Giakoumis, M. Bosch-Jorge, R. J. Ros, R. L. Tarazón, S. Likothanassis, and D. Tzovaras, “Pallet detection and docking strategy for autonomous pallet truck agv operation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 3444–3451.
  14. L. Kästner, B. Fatloun, Z. Shen, D. Gawrisch, and J. Lambrecht, “Human-following and-guiding in crowded environments using semantic deep-reinforcement-learning for mobile service robots,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 833–839.
  15. Z. Chen, T. Zhang, and C. Ouyang, “End-to-end airplane detection using transfer learning in remote sensing images,” Remote Sensing, vol. 10, no. 1, p. 139, 2018.
  16. S. Lee and S.-W. Seo, “Sensor fusion for aircraft detection at airport ramps using conditional random fields,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18 100–18 112, 2022.
  17. Y. Hu, W. Zhan, and M. Tomizuka, “Probabilistic prediction of vehicle semantic intention and motion,” in 2018 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2018, pp. 307–313.
  18. S. Lee and S.-W. Seo, “Probabilistic context integration-based aircraft behaviour intention classification at airport ramps,” IET Intelligent Transport Systems, vol. 16, no. 6, pp. 725–738, 2022.
  19. S. Gilroy, E. Jones, and M. Glavin, “Overcoming occlusion in the automotive environment—a review,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 1, pp. 23–35, 2019.
  20. H. Park, J. Choi, H. Chin, S.-H. Lee, and D. Baek, “Occlusion-aware risk assessment and driving strategy for autonomous vehicles using simplified reachability quantification,” IEEE Robotics and Automation Letters, vol. 8, no. 12, pp. 8486–8493, 2023.
  21. J. Choi, H. Chin, H. Park, D. Kwon, S. Lee, and D. Baek, “Safe and efficient trajectory optimization for autonomous vehicles using b-spline with incremental path flattening,” arXiv preprint arXiv:2311.02957, 2023.
  22. R. Deits and R. Tedrake, “Efficient mixed-integer planning for uavs in cluttered environments,” in 2015 IEEE international conference on robotics and automation (ICRA).   IEEE, 2015, pp. 42–49.
  23. B. Li, Y. Zhang, T. Zhang, T. Acarman, Y. Ouyang, L. Li, H. Dong, and D. Cao, “Embodied footprints: A safety-guaranteed collision avoidance model for numerical optimization-based trajectory planning,” arXiv preprint arXiv:2302.07622, 2023.
  24. S.-H. Lee, Y. Jung, and S.-W. Seo, “Imagination-augmented hierarchical reinforcement learning for safe and interactive autonomous driving in urban environments,” arXiv preprint arXiv:2311.10309, 2023.
  25. M. Werling, J. Ziegler, S. Kammel, and S. Thrun, “Optimal trajectory generation for dynamic street scenarios in a frenét frame,” in 2010 IEEE International Conference on Robotics and Automation, 2010, pp. 987–993.
  26. J. Yan, Y. Liu, J. Sun, F. Jia, S. Li, T. Wang, and X. Zhang, “Cross modal transformer: Towards fast and robust 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 18 268–18 278.
  27. V.-T. Tran and W.-H. Tsai, “Audio-vision emergency vehicle detection,” IEEE Sensors Journal, vol. 21, no. 24, pp. 27 905–27 917, 2021.
  28. C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International Conference on Machine Learning.   PMLR, 2017, pp. 1126–1135.
  29. A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn, “Learning to adapt in dynamic, real-world environments through meta-reinforcement learning,” in International Conference on Learning Representations, 2018.
  30. C. Medina Sánchez, M. Zella, J. Capitán, and P. J. Marrón, “From perception to navigation in environments with persons: An indoor evaluation of the state of the art,” Sensors, vol. 22, no. 3, p. 1191, 2022.
  31. C. Chen, Y. Liu, S. Kreiss, and A. Alahi, “Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning,” in 2019 international conference on robotics and automation (ICRA).   IEEE, 2019, pp. 6015–6022.
  32. A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning.   PMLR, 2017, pp. 1–16.
  33. P. Kaur, Z. Liu, and W. Shi, “Simulators for mobile social robots: State-of-the-art and challenges,” in 2022 Fifth International Conference on Connected and Autonomous Driving (MetroCAD).   IEEE, 2022, pp. 47–56.
  34. F. Camara, N. Bellotto, S. Cosar, F. Weber, D. Nathanael, M. Althoff, J. Wu, J. Ruenz, A. Dietrich, G. Markkula et al., “Pedestrian models for autonomous driving part ii: high-level models of human behavior,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 9, pp. 5453–5472, 2020.
  35. A. Sharma, K. Xu, N. Sardana, A. Gupta, K. Hausman, S. Levine, and C. Finn, “Autonomous reinforcement learning: Formalism and benchmarking,” in International Conference on Learning Representations, 2021.
  36. K. Cho and S. Oh, “Learning-based model predictive control under signal temporal logic specifications,” in 2018 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2018, pp. 7322–7329.
  37. S.-H. Lee and S.-W. Seo, “A learning-based framework for handling dilemmas in urban automated driving,” in 2017 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2017, pp. 1436–1442.
  38. K. Kim, S. Cho, and W. Chung, “Hd map update for autonomous driving with crowdsourced data,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1895–1901, 2021.
Citations (1)
View on Semantic Scholar

Summary

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

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now