Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
117 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction (2403.11695v1)

Published 18 Mar 2024 in cs.CV

Abstract: Autonomous driving systems are a rapidly evolving technology that enables driverless car production. Trajectory prediction is a critical component of autonomous driving systems, enabling cars to anticipate the movements of surrounding objects for safe navigation. Trajectory prediction using Lidar point-cloud data performs better than 2D images due to providing 3D information. However, processing point-cloud data is more complicated and time-consuming than 2D images. Hence, state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. This paper introduces TrajectoryNAS, a pioneering method that focuses on utilizing point cloud data for trajectory prediction. By leveraging Neural Architecture Search (NAS), TrajectoryNAS automates the design of trajectory prediction models, encompassing object detection, tracking, and forecasting in a cohesive manner. This approach not only addresses the complex interdependencies among these tasks but also emphasizes the importance of accuracy and efficiency in trajectory modeling. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field.Experimental results reveal that TrajcetoryNAS yield a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (40)
  1. M. Liang, B. Yang, W. Zeng, Y. Chen, R. Hu, S. Casas, and R. Urtasun, “Pnpnet: End-to-end perception and prediction with tracking in the loop,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 11 553–11 562.
  2. L. L. Li, B. Yang, M. Liang, W. Zeng, M. Ren, S. Segal, and R. Urtasun, “End-to-end contextual perception and prediction with interaction transformer,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 5784–5791.
  3. F. Marchetti, F. Becattini, L. Seidenari, and A. Del Bimbo, “Multiple trajectory prediction of moving agents with memory augmented networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
  4. H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” arXiv preprint arXiv:1903.11027, 2019.
  5. M.-F. Chang, J. W. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays, “Argoverse: 3d tracking and forecasting with rich maps,” in Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  6. F. Leon and M. Gavrilescu, “A review of tracking and trajectory prediction methods for autonomous driving,” Mathematics, vol. 9, no. 6, p. 660, 2021.
  7. T. Phan-Minh, E. C. Grigore, F. A. Boulton, O. Beijbom, and E. M. Wolff, “Covernet: Multimodal behavior prediction using trajectory sets,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 14 074–14 083.
  8. J. Gao, C. Sun, H. Zhao, Y. Shen, D. Anguelov, C. Li, and C. Schmid, “Vectornet: Encoding hd maps and agent dynamics from vectorized representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 11 525–11 533.
  9. M. Liang, B. Yang, R. Hu, Y. Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16.   Springer, 2020, pp. 541–556.
  10. M. Ye, T. Cao, and Q. Chen, “Tpcn: Temporal point cloud networks for motion forecasting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 11 318–11 327.
  11. K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu et al., “A survey on vision transformer,” IEEE transactions on pattern analysis and machine intelligence, vol. 45, no. 1, pp. 87–110, 2022.
  12. Y. Yuan, X. Weng, Y. Ou, and K. M. Kitani, “Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 9813–9823.
  13. S. Khandelwal, W. Qi, J. Singh, A. Hartnett, and D. Ramanan, “What-if motion prediction for autonomous driving,” arXiv preprint arXiv:2008.10587, 2020.
  14. X. Weng, B. Ivanovic, K. Kitani, and M. Pavone, “Whose track is it anyway? improving robustness to tracking errors with affinity-based trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 6573–6582.
  15. S. Wang, Y. Sun, C. Liu, and M. Liu, “Pointtracknet: An end-to-end network for 3-d object detection and tracking from point clouds,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3206–3212, 2020.
  16. T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 11 784–11 793.
  17. X. Li and J. E. Guivant, “Efficient and accurate object detection with simultaneous classification and tracking under limited computing power,” IEEE Transactions on Intelligent Transportation Systems, 2023.
  18. M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz, and H. Michael Gross, “Complexer-yolo: Real-time 3d object detection and tracking on semantic point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019, pp. 0–0.
  19. X. Weng, Y. Yuan, and K. Kitani, “Ptp: Parallelized tracking and prediction with graph neural networks and diversity sampling,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4640–4647, 2021.
  20. W. Luo, B. Yang, and R. Urtasun, “Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2018, pp. 3569–3577.
  21. S. Casas, W. Luo, and R. Urtasun, “Intentnet: Learning to predict intention from raw sensor data,” in Conference on Robot Learning.   PMLR, 2018, pp. 947–956.
  22. W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun, “End-to-end interpretable neural motion planner,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 8660–8669.
  23. X. Weng, J. Wang, S. Levine, K. Kitani, and N. Rhinehart, “Inverting the pose forecasting pipeline with spf2: Sequential pointcloud forecasting for sequential pose forecasting,” in Conference on robot learning.   PMLR, 2021, pp. 11–20.
  24. N. Peri, J. Luiten, M. Li, A. Ošep, L. Leal-Taixé, and D. Ramanan, “Forecasting from lidar via future object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 17 202–17 211.
  25. T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021, pp. 11 784–11 793.
  26. X. He, K. Zhao, and X. Chu, “Automl: A survey of the state-of-the-art,” Knowledge-Based Systems, vol. 212, p. 106622, 2021.
  27. T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,” The Journal of Machine Learning Research, vol. 20, no. 1, pp. 1997–2017, 2019.
  28. B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” arXiv preprint arXiv:1611.01578, 2016.
  29. C.-H. Hsu, S.-H. Chang, J.-H. Liang, H.-P. Chou, C.-H. Liu, S.-C. Chang, J.-Y. Pan, Y.-T. Chen, W. Wei, and D.-C. Juan, “Monas: Multi-objective neural architecture search using reinforcement learning,” arXiv preprint arXiv:1806.10332, 2018.
  30. M. Loni, S. Sinaei, A. Zoljodi, M. Daneshtalab, and M. Sjödin, “Deepmaker: A multi-objective optimization framework for deep neural networks in embedded systems,” Microprocessors and Microsystems, vol. 73, p. 102989, 2020.
  31. M. Loni, A. Zoljodi, S. Sinaei, M. Daneshtalab, and M. Sjödin, “Neuropower: Designing energy efficient convolutional neural network architecture for embedded systems,” in International Conference on Artificial Neural Networks.   Springer, 2019, pp. 208–222.
  32. C. Liu, L.-C. Chen, F. Schroff, H. Adam, W. Hua, A. L. Yuille, and L. Fei-Fei, “Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 82–92.
  33. H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” arXiv preprint arXiv:1806.09055, 2018.
  34. M. Loni, H. Mousavi, M. Riazati, M. Daneshtalab, and M. Sjödin, “Tas:ternarized neural architecture search for resource-constrained edge devices,” in Design, Automation & Test in Europe Conference & Exhibition DATE’22, 14 March 2022, Antwerp, Belgium.   IEEE, March 2022. [Online]. Available: http://www.es.mdh.se/publications/6351-
  35. H. Cai, C. Gan, T. Wang, Z. Zhang, and S. Han, “Once-for-all: Train one network and specialize it for efficient deployment,” arXiv preprint arXiv:1908.09791, 2019.
  36. X. Dong, L. Liu, K. Musial, and B. Gabrys, “Nats-bench: Benchmarking nas algorithms for architecture topology and size,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021.
  37. M. Loni, A. Zoljodi, D. Maier, A. Majd, M. Daneshtalab, M. Sjödin, B. Juurlink, and R. Akbari, “Densedisp: Resource-aware disparity map estimation by compressing siamese neural architecture,” in 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8.
  38. H. Xu, S. Wang, X. Cai, W. Zhang, X. Liang, and Z. Li, “Curvelane-nas: Unifying lane-sensitive architecture search and adaptive point blending,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16.   Springer, 2020, pp. 689–704.
  39. M. Loni, A. Zoljodi, A. Majd, B. H. Ahn, M. Daneshtalab, M. Sjödin, and H. Esmaeilzadeh, “Faststereonet: A fast neural architecture search for improving the inference of disparity estimation on resource-limited platforms,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–13, 2021.
  40. K. Amine, “Multiobjective simulated annealing: Principles and algorithm variants,” Advances in Operations Research, vol. 2019, 2019.
Citations (2)
View on Semantic Scholar

Summary

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

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