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Unified End-to-End V2X Cooperative Autonomous Driving (2405.03971v1)

Published 7 May 2024 in cs.CV and cs.MA

Abstract: V2X cooperation, through the integration of sensor data from both vehicles and infrastructure, is considered a pivotal approach to advancing autonomous driving technology. Current research primarily focuses on enhancing perception accuracy, often overlooking the systematic improvement of accident prediction accuracy through end-to-end learning, leading to insufficient attention to the safety issues of autonomous driving. To address this challenge, this paper introduces the UniE2EV2X framework, a V2X-integrated end-to-end autonomous driving system that consolidates key driving modules within a unified network. The framework employs a deformable attention-based data fusion strategy, effectively facilitating cooperation between vehicles and infrastructure. The main advantages include: 1) significantly enhancing agents' perception and motion prediction capabilities, thereby improving the accuracy of accident predictions; 2) ensuring high reliability in the data fusion process; 3) superior end-to-end perception compared to modular approaches. Furthermore, We implement the UniE2EV2X framework on the challenging DeepAccident, a simulation dataset designed for V2X cooperative driving.

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References (51)
  1. Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives. Transportation research part C: emerging technologies, 127:103101, 2021.
  2. Milestones in autonomous driving and intelligent vehicles: Survey of surveys. IEEE Transactions on Intelligent Vehicles, 8(2):1046–1056, 2022.
  3. A survey of autonomous driving: Common practices and emerging technologies. IEEE access, 8:58443–58469, 2020.
  4. Applying deep learning to autonomous vehicles: A survey. In 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), pages 247–252, 2021.
  5. Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review. arXiv preprint arXiv:2308.05731, 2023.
  6. Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6):4909–4926, 2021.
  7. A systematic survey of control techniques and applications in connected and automated vehicles. IEEE Internet of Things Journal, 2023.
  8. A novel fuzzy observer-based steering control approach for path tracking in autonomous vehicles. IEEE Transactions on Fuzzy Systems, 27(2):278–290, 2018.
  9. Multi-sensor fusion in automated driving: A survey. Ieee Access, 8:2847–2868, 2019.
  10. Multi-modal sensor fusion for auto driving perception: A survey. arXiv preprint arXiv:2202.02703, 2022.
  11. Overview and prospect of communication-sensing-computing integration for autonomous driving in the internet of vehicles. Chinese Journal of Engineering, 45(1):137–149, 2023.
  12. A survey of collaborative machine learning using 5g vehicular communications. IEEE Communications Surveys & Tutorials, 24(2):1280–1303, 2022.
  13. A comprehensive survey on the applications of blockchain for securing vehicular networks. IEEE Communications Surveys & Tutorials, 24(2):1212–1239, 2022.
  14. Leveraging the edge and cloud for v2x-based real-time object detection in autonomous driving. Computer Communications, 213:372–381, 2024.
  15. Delving into the devils of bird’s-eye-view perception: A review, evaluation and recipe. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
  16. Collaborative perception in autonomous driving: Methods, datasets, and challenges. IEEE Intelligent Transportation Systems Magazine, 2023.
  17. Deep learning-based vehicle behavior prediction for autonomous driving applications: A review. IEEE Transactions on Intelligent Transportation Systems, 23(1):33–47, 2020.
  18. An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors. IEEE transactions on vehicular technology, 69(12):14458–14469, 2020.
  19. A survey of deep learning applications to autonomous vehicle control. IEEE Transactions on Intelligent Transportation Systems, 22(2):712–733, 2020.
  20. Autonomous driving system: A comprehensive survey. Expert Systems with Applications, page 122836, 2023.
  21. Fully end-to-end autonomous driving with semantic depth cloud mapping and multi-agent. arXiv preprint arXiv:2204.05513, 2022.
  22. Recent advancements in end-to-end autonomous driving using deep learning: A survey. IEEE Transactions on Intelligent Vehicles, 2023.
  23. Motion planning for autonomous driving: The state of the art and future perspectives. IEEE Transactions on Intelligent Vehicles, 2023.
  24. A review of end-to-end autonomous driving in urban environments. IEEE Access, 10:75296–75311, 2022.
  25. A vision of c-v2x: Technologies, field testing, and challenges with chinese development. IEEE Internet of Things Journal, 7(5):3872–3881, 2020.
  26. Challenges and solutions for cellular based v2x communications. IEEE Communications Surveys & Tutorials, 23(1):222–255, 2020.
  27. A tutorial on 5g nr v2x communications. IEEE Communications Surveys & Tutorials, 23(3):1972–2026, 2021.
  28. A survey of end-to-end driving: Architectures and training methods. IEEE Transactions on Neural Networks and Learning Systems, 33(4):1364–1384, 2020.
  29. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316, 2016.
  30. End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions. In 2018 24th international conference on pattern recognition (ICPR), pages 2289–2294. IEEE, 2018.
  31. Exploring the limitations of behavior cloning for autonomous driving. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9329–9338, 2019.
  32. Learning urban driving policies using deep reinforcement learning. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pages 607–614. IEEE, 2021.
  33. Policy-based reinforcement learning for training autonomous driving agents in urban areas with affordance learning. IEEE Transactions on Intelligent Transportation Systems, 23(8):12562–12571, 2021.
  34. Multi-modal policy fusion for end-to-end autonomous driving. Information Fusion, 98:101834, 2023.
  35. Multimodal end-to-end autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 23(1):537–547, 2020.
  36. Fusionad: Multi-modality fusion for prediction and planning tasks of autonomous driving. arXiv preprint arXiv:2308.01006, 2023.
  37. Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 23(6):5068–5078, 2021.
  38. Planning-oriented autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17853–17862, 2023.
  39. Vehicle-to-everything (v2x) in the autonomous vehicles domain–a technical review of communication, sensor, and ai technologies for road user safety. Transportation Research Interdisciplinary Perspectives, 23:100980, 2024.
  40. Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pages 514–524. IEEE, 2019.
  41. Who2com: Collaborative perception via learnable handshake communication. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 6876–6883. IEEE, 2020.
  42. When2com: Multi-agent perception via communication graph grouping. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 4106–4115, 2020.
  43. Where2comm: Communication-efficient collaborative perception via spatial confidence maps. Advances in neural information processing systems, 35:4874–4886, 2022.
  44. V2vnet: Vehicle-to-vehicle communication for joint perception and prediction. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, pages 605–621. Springer, 2020.
  45. V2vformer +⁣++++ +: Multi-modal vehicle-to-vehicle cooperative perception via global-local transformer. IEEE Transactions on Intelligent Transportation Systems, 2023.
  46. Trupercept: Trust modelling for autonomous vehicle cooperative perception from synthetic data. In 2020 IEEE Intelligent Vehicles Symposium (IV), pages 341–347. IEEE, 2020.
  47. V2x-sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving. IEEE Robotics and Automation Letters, 7(4):10914–10921, 2022.
  48. Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In 2022 International Conference on Robotics and Automation (ICRA), pages 2583–2589. IEEE, 2022.
  49. Vehicle-infrastructure cooperative 3d object detection via feature flow prediction. arXiv preprint arXiv:2303.10552, 2023.
  50. Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 5599–5606, 2024.
  51. Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers. In European conference on computer vision, pages 1–18. Springer, 2022.

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