Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 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

A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving (2402.19251v1)

Published 29 Feb 2024 in cs.AI and cs.RO

Abstract: In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency. Incorporating human decision-making insights enables AVs to more effectively anticipate the potential actions of other vehicles, significantly improving prediction accuracy and responsiveness in dynamic environments. This paper introduces the Human-Like Trajectory Prediction (HLTP) model, which adopts a teacher-student knowledge distillation framework inspired by human cognitive processes. The HLTP model incorporates a sophisticated teacher-student knowledge distillation framework. The "teacher" model, equipped with an adaptive visual sector, mimics the visual processing of the human brain, particularly the functions of the occipital and temporal lobes. The "student" model focuses on real-time interaction and decision-making, drawing parallels to prefrontal and parietal cortex functions. This approach allows for dynamic adaptation to changing driving scenarios, capturing essential perceptual cues for accurate prediction. Evaluated using the Macao Connected and Autonomous Driving (MoCAD) dataset, along with the NGSIM and HighD benchmarks, HLTP demonstrates superior performance compared to existing models, particularly in challenging environments with incomplete data. The project page is available at Github.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (52)
  1. Y. Huang, J. Du, Z. Yang, Z. Zhou, L. Zhang, and H. Chen, “A survey on trajectory-prediction methods for autonomous driving,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 3, pp. 652–674, 2022.
  2. S. W. Loke, “Cooperative automated vehicles: A review of opportunities and challenges in socially intelligent vehicles beyond networking,” IEEE Transactions on Intelligent Vehicles, vol. 4, no. 4, pp. 509–518, 2019.
  3. A. Miller, A. Fisch, J. Dodge, A.-H. Karimi, A. Bordes, and J. Weston, “Key-value memory networks for directly reading documents,” arXiv preprint arXiv:1606.03126, 2016.
  4. M. Gao, L. Jin, Y. Jiang, and J. Bie, “Multiple object tracking using a dual-attention network for autonomous driving,” IET Intelligent Transport Systems, vol. 14, no. 8, pp. 842–848, 2020.
  5. L. Hou, S. E. Li, B. Yang, Z. Wang, and K. Nakano, “Integrated graphical representation of highway scenarios to improve trajectory prediction of surrounding vehicles,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1638–1651, 2023.
  6. R. Quan, L. Zhu, Y. Wu, and Y. Yang, “Holistic lstm for pedestrian trajectory prediction,” IEEE transactions on image processing, vol. 30, pp. 3229–3239, 2021.
  7. D. Xu, X. Shang, Y. Liu, H. Peng, and H. Li, “Group vehicle trajectory prediction with global spatio-temporal graph,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1219–1229, 2023.
  8. X. Li, X. Ying, and M. C. Chuah, “Grip: Graph-based interaction-aware trajectory prediction,” in ITSC.   IEEE, 2019.
  9. C. Chang, J. Zhang, K. Zhang, W. Zhong, X. Peng, S. Li, and L. Li, “Bev-v2x: Cooperative birds-eye-view fusion and grid occupancy prediction via v2x-based data sharing,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 11, pp. 4498–4514, 2023.
  10. X. Chen, H. Zhang, F. Zhao, Y. Hu, C. Tan, and J. Yang, “Intention-aware vehicle trajectory prediction based on spatial-temporal dynamic attention network for internet of vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 19 471–19 483, 2022.
  11. K. Wu, Y. Zhou, H. Shi, X. Li, and B. Ran, “Graph-based interaction-aware multimodal 2d vehicle trajectory prediction using diffusion graph convolutional networks,” IEEE Transactions on Intelligent Vehicles, 2023.
  12. D. Yang, H. Zhang, E. Yurtsever, K. A. Redmill, and Ü. Özgüner, “Predicting pedestrian crossing intention with feature fusion and spatio-temporal attention,” IEEE Transactions on Intelligent Vehicles, 2022.
  13. Z. Li, Z. Chen, Y. Li, and C. Xu, “Context-aware trajectory prediction for autonomous driving in heterogeneous environments,” Computer-Aided Civil and Infrastructure Engineering, 2023.
  14. Z. Zhou, J. Wang, Y.-H. Li, and Y.-K. Huang, “Query-centric trajectory prediction,” in Proceedings of the IEEE/CVF CVPR, 2023.
  15. H. Liao, Z. Li, H. Shen, W. Zeng, G. Li, S. E. Li, and C. Xu, “Bat: Behavior-aware human-like trajectory prediction for autonomous driving,” arXiv preprint arXiv:2312.06371, 2023.
  16. Y. Yan, L. Peng, T. Shen, J. Wang, D. Pi, D. Cao, and G. Yin, “A multi-vehicle game-theoretic framework for decision making and planning of autonomous vehicles in mixed traffic,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 11, pp. 4572–4587, 2023.
  17. A. Kamenev, L. Wang, O. B. Bohan, I. Kulkarni, B. Kartal, A. Molchanov, S. Birchfield, D. Nistér, and N. Smolyanskiy, “Predictionnet: Real-time joint probabilistic traffic prediction for planning, control, and simulation,” 2022.
  18. X. Chen, W. Zhang, H. Bai, C. Xu, H. Ding, and W. Huang, “Two-dimensional following lane-changing (2df-lc): A framework for dynamic decision-making and rapid behavior planning,” IEEE Transactions on Intelligent Vehicles, 2023.
  19. H. Liao, S. Liu, Y. Li, Z. Li, C. Wang, B. Wang, Y. Guan, and C. Xu, “Human observation-inspired trajectory prediction for autonomous driving in mixed-autonomy traffic environments,” arXiv preprint arXiv:2402.04318, 2024.
  20. Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, L. Lu, X. Jia, Q. Liu, J. Dai, Y. Qiao, and H. Li, “Planning-oriented autonomous driving,” 2023.
  21. H. Liao, H. Shen, Z. Li, C. Wang, G. Li, Y. Bie, and C. Xu, “Gpt-4 enhanced multimodal grounding for autonomous driving: Leveraging cross-modal attention with large language models,” arXiv preprint arXiv:2312.03543, 2023.
  22. G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.
  23. A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “Fitnets: Hints for thin deep nets,” arXiv preprint arXiv:1412.6550, 2014.
  24. S. Zagoruyko and N. Komodakis, “Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer,” arXiv preprint arXiv:1612.03928, 2016.
  25. T. Furlanello, Z. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, “Born again neural networks,” in International Conference on Machine Learning.   PMLR, 2018, pp. 1607–1616.
  26. S. Bhardwaj, M. Srinivasan, and M. M. Khapra, “Efficient video classification using fewer frames,” in Proceedings of the IEEE/CVF CVPR, 2019, pp. 354–363.
  27. G. Li, Z. Ji, S. Li, X. Luo, and X. Qu, “Driver behavioral cloning for route following in autonomous vehicles using task knowledge distillation,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1025–1033, 2023.
  28. A. Monti, A. Porrello, S. Calderara, P. Coscia, L. Ballan, and R. Cucchiara, “How many observations are enough? knowledge distillation for trajectory forecasting,” in Proceedings of the IEEE/CVF CVPR, 2022.
  29. R. Wang, S. Wang, H. Yan, and X. Wang, “Wsip: wave superposition inspired pooling for dynamic interactions-aware trajectory prediction,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 4, 2023, pp. 4685–4692.
  30. A. Tucker and K. Marsh, “Speeding through the pandemic: Perceptual and psychological factors associated with speeding during the covid-19 stay-at-home period,” Accident Analysis & Prevention, vol. 159, p. 106225, 2021.
  31. Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10 012–10 022.
  32. A. Kendall, Y. Gal, and R. Cipolla, “Multi-task learning using uncertainty to weigh losses for scene geometry and semantics,” 2018.
  33. N. Deo and M. M. Trivedi, “Convolutional social pooling for vehicle trajectory prediction,” in Proceedings of the IEEE CVPR workshops, 2018, pp. 1468–1476.
  34. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013.
  35. R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,” in ITSC, 2018.
  36. X. Huang, X. Cheng, Q. Geng, B. Cao, D. Zhou, P. Wang, Y. Lin, and R. Yang, “The apolloscape dataset for autonomous driving,” in Proceedings of the IEEE CVPR workshops, 2018, pp. 954–960.
  37. M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan et al., “Argoverse: 3d tracking and forecasting with rich maps,” in Proceedings of the IEEE/CVF CVPR, 2019, pp. 8748–8757.
  38. R. Krajewski, T. Moers, J. Bock, L. Vater, and L. Eckstein, “The round dataset: A drone dataset of road user trajectories at roundabouts in germany,” in ITSC, 2020.
  39. 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,” in the IEEE/CVF CVPR, 2020.
  40. A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial networks,” in Proceedings of the IEEE CVPR, 2018, pp. 2255–2264.
  41. T. Zhao, Y. Xu, M. Monfort, W. Choi, C. Baker, Y. Zhao, Y. Wang, and Y. N. Wu, “Multi-agent tensor fusion for contextual trajectory prediction,” in Proceedings of the IEEE/CVF CVPR, 2019.
  42. V. Lefkopoulos, M. Menner, A. Domahidi, and M. N. Zeilinger, “Interaction-aware motion prediction for autonomous driving: A multiple model kalman filtering scheme,” IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 80–87, 2020.
  43. C. Tang and R. R. Salakhutdinov, “Multiple futures prediction,” Advances in neural information processing systems, vol. 32, 2019.
  44. K. Gao, X. Li, B. Chen, L. Hu, J. Liu, R. Du, and Y. Li, “Dual transformer based prediction for lane change intentions and trajectories in mixed traffic environment,” IEEE Transactions on Intelligent Transportation Systems, 2023.
  45. X. Xie, C. Zhang, Y. Zhu, Y. N. Wu, and S.-C. Zhu, “Congestion-aware multi-agent trajectory prediction for collision avoidance,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 13 693–13 700.
  46. K. Messaoud, I. Yahiaoui, A. Verroust-Blondet, and F. Nashashibi, “Attention based vehicle trajectory prediction,” IEEE Transactions on Intelligent Vehicles, vol. 6, no. 1, pp. 175–185, 2021.
  47. Q. Xue, S. Li, X. Li, J. Zhao, and W. Zhang, “Hierarchical motion encoder-decoder network for trajectory forecasting,” arXiv preprint arXiv:2111.13324, 2021.
  48. Y. Wang, S. Zhao, R. Zhang, X. Cheng, and L. Yang, “Multi-vehicle collaborative learning for trajectory prediction with spatio-temporal tensor fusion,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 236–248, 2022.
  49. X. Chen, H. Zhang, F. Zhao, Y. Cai, H. Wang, and Q. Ye, “Vehicle trajectory prediction based on intention-aware non-autoregressive transformer with multi-attention learning for internet of vehicles,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–12, 2022.
  50. P. Cong, Y. Xiao, X. Wan, M. Deng, J. Li, and X. Zhang, “Dacr-amtp: Adaptive multi-modal vehicle trajectory prediction for dynamic drivable areas based on collision risk,” IEEE Transactions on Intelligent Vehicles, 2023.
  51. Z. Zuo, X. Wang, S. Guo, Z. Liu, Z. Li, and Y. Wang, “Trajectory prediction network of autonomous vehicles with fusion of historical interactive features,” IEEE Transactions on Intelligent Vehicles, 2023.
  52. Y. Cai, Z. Wang, H. Wang, L. Chen, Y. Li, M. A. Sotelo, and Z. Li, “Environment-attention network for vehicle trajectory prediction,” IEEE Transactions on Vehicular Technology, 2021.
Citations (11)
View on Semantic Scholar

Summary

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

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