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One-Shot Learning of Visual Path Navigation for Autonomous Vehicles

Published 15 Jun 2023 in cs.CV and cs.LG | (2306.08865v1)

Abstract: Autonomous driving presents many challenges due to the large number of scenarios the autonomous vehicle (AV) may encounter. End-to-end deep learning models are comparatively simplistic models that can handle a broad set of scenarios. However, end-to-end models require large amounts of diverse data to perform well. This paper presents a novel deep neural network that performs image-to-steering path navigation that helps with the data problem by adding one-shot learning to the system. Presented with a previously unseen path, the vehicle can drive the path autonomously after being shown the path once and without model retraining. In fact, the full path is not needed and images of the road junctions is sufficient. In-vehicle testing and offline testing are used to verify the performance of the proposed navigation and to compare different candidate architectures.

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References (29)
  1. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255.
  2. X. Zhai, A. Kolesnikov, N. Houlsby, and L. Beyer, “Scaling vision transformers,” CoRR, vol. abs/2106.04560, 2021. [Online]. Available: https://arxiv.org/abs/2106.04560
  3. M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang et al., “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316, 2016.
  4. N. Merrill and A. Eskandarian, “End-to-End Multi-Task Machine Learning of Vehicle Dynamics for Steering Angle Prediction for Autonomous Driving,” in ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 08 2019.
  5. J. Solomon and F. Charette, “Hierarchical multi-task deep neural network architecture for end-to-end driving,” ArXiv, vol. abs/1902.03466, 2019.
  6. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, “Human-level concept learning through probabilistic program induction,” Science, vol. 350, no. 6266, pp. 1332–1338, 2015.
  7. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, “Meta-learning with memory-augmented neural networks,” in Proceedings of The 33rd International Conference on Machine Learning, vol. 48.   PMLR, 2016, pp. 1842–1850.
  8. G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” ICML Deep Learning workshop, 2015.
  9. O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra, “Matching networks for one shot learning,” Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3630–3638, 2016.
  10. J. X. Wang, Z. Kurth-Nelson, H. Soyer, J. Z. Leibo, D. Tirumala, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick, “Learning to reinforcement learn,” ArXiv, vol. abs/1611.05763, 2017.
  11. C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the 34th International Conference on Machine Learning, vol. 70.   PMLR, 2017, pp. 1126–1135.
  12. Q. Luo, M. Sorokin, and S. Ha, “A few shot adaptation of visual navigation skills to new observations using meta-learning,” ArXiv, vol. abs/2011.03609, 2020.
  13. A. Majee, K. Agrawal, and A. Subramanian, “Few-shot learning for road object detection,” CoRR, vol. abs/2101.12543, 2021. [Online]. Available: https://arxiv.org/abs/2101.12543
  14. J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
  15. F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales, “Learning to compare: Relation network for few-shot learning,” in The IEEE Conference on Computer Vision and Pattern Recognition, June 2018.
  16. S. Ruder, “An overview of multi-task learning in deep neural networks,” ArXiv, vol. abs/1706.05098, 2017.
  17. S. Chowdhuri, T. Pankaj, and K. Zipser, “Multi-modal multi-task deep learning for autonomous driving,” ArXiv, vol. abs/1709.05581, 2017.
  18. Z. Yang, Y. Zhang, J. Yu, J. Cai, and J. Luo, “End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions,” 2018 24th International Conference on Pattern Recognition, pp. 2289–2294, 2018.
  19. P. Furgale and T. Barfoot, “Visual teach and repeat for long-range rover autonomy,” J. Field Robotics, vol. 27, pp. 534–560, 09 2010.
  20. D. Dall’Osto, T. Fischer, and M. Milford, “Fast and robust bio-inspired teach and repeat navigation,” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 500–507, 2021.
  21. H. Karnan, G. Warnell, X. Xiao, and P. Stone, “Voila: Visual-observation-only imitation learning for autonomous navigation,” 2022 International Conference on Robotics and Automation, pp. 2497–2503, 2022.
  22. T. H. Group, “Hierarchical data format version 5,” http://www.hdfgroup.org/HDF5, 2010, accessed: 2021-09-15.
  23. W. Xu, Y. Xu, H. Wang, and Z. Tu, “Attentional constellation nets for few-shot learning,” in International Conference on Learning Representations, 2021.
  24. Y. Du, X. Zhen, L. Shao, and C. G. M. Snoek, “Hierarchical variational memory for few-shot learning across domains,” in International Conference on Learning Representations, 2022.
  25. M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” Proceedings of the 34th International Conference on Machine Learning, 2017.
  26. K. Q. Weinberger, J. Blitzer, and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” in Advances in Neural Information Processing Systems, 2006.
  27. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, pp. 1735–80, 12 1997.
  28. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
  29. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 12 2015, pp. 4489–4497.
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