mlsanityData to 7 Oct 2026

COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles

arXiv 2205.02222 ↗First posted 4 May 2022cs.CV cs.ROcited by 95

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today's autonomous vehicles is hindered by the limited line-of-sight sensing capability and the brittleness of data-driven methods in handling extreme situations. With recent developments of telecommunication technologies, cooperative perception with vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency situations. We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our model encodes LiDAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless channels. To evaluate our model, we develop AutoCastSim, a network-augmented driving simulation framework with example accident-prone scenarios. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate over egocentric driving models in these challenging driving situations and a 5 times smaller bandwidth requirement than prior work V2VNet. COOPERNAUT and AUTOCASTSIM are available at https://ut-austin-rpl.github.io/Coopernaut/.

Key facts. Cited by 95 papers in the map (data through 7 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 26 of its 48 references that are in the map. First posted on arXiv 4 May 2022.

Builds on

26 of 48 references.

References · 26

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  5. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  6. 2020Point Transformercited by 1,462
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2016End to End Learning for Self-Driving Carscited by 1,000
  9. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  10. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  11. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  12. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
14 more
  1. 2019Learning by Cheatingcited by 361
  2. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
  3. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  4. 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
  5. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  6. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  7. 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
  8. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 147
  9. 2019End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordancescited by 147
  10. 2021Learning to drive from a world on railscited by 114
  11. 2020HDNET: Exploiting HD Maps for 3D Object Detectioncited by 78
  12. 2018Conditional Affordance Learning for Driving in Urban Environmentscited by 70
  13. 2021AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Drivingcited by 26
  14. 2020Cooperative Perception with Deep Reinforcement Learning for Connected Vehiclescited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 27; 2024: 24; 2025: 24; 2026: 15.

95 papers.

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  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  2. 2023V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perceptioncited by 155
  3. 2023V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecastingcited by 114
  4. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  5. 2023ReasonNet: End-to-End Driving with Temporal and Global Reasoningcited by 92
  6. 2023Collaborative Perception in Autonomous Driving: Methods, Datasets and Challengescited by 87
  7. 2024An Extensible Framework for Open Heterogeneous Collaborative Perceptioncited by 87
  8. 2022Latency-Aware Collaborative Perceptioncited by 86
  9. 2023BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detectioncited by 74
  10. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 65
  11. 2022Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communicationcited by 47
  12. 2024V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Modelscited by 47
38 more
  1. 2025STAMP: Scalable Task And Model-agnostic Collaborative Perceptioncited by 44
  2. 2024End-to-End Autonomous Driving through V2X Cooperationcited by 42
  3. 2023CORE: Cooperative Reconstruction for Multi-Agent Perceptioncited by 41
  4. 2023Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perceptioncited by 38
  5. 2024RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perceptioncited by 37
  6. 2023Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detectioncited by 31
  7. 2023UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Frameworkcited by 29
  8. 2022V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perceptioncited by 28
  9. 2025V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Modelscited by 27
  10. 2023Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Predictioncited by 25
  11. 2023Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Drivingcited by 25
  12. 2024CMP: Cooperative Motion Prediction with Multi-Agent Communicationcited by 25
  13. 2025LangCoop: Collaborative Driving with Languagecited by 25
  14. 2023QUEST: Query Stream for Practical Cooperative Perceptioncited by 23
  15. 2024VLM-Auto: VLM-based Autonomous Driving Assistant with Human-like Behavior and Understanding for Complex Road Scenescited by 22
  16. 2022A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperationcited by 21
  17. 2023BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalitiescited by 20
  18. 2024Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End Systemcited by 20
  19. 2023AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Modelcited by 17
  20. 2023Vehicle-to-Everything Cooperative Perception for Autonomous Drivingcited by 15
  21. 2024AD-H: Language-guided Autonomous Driving with Hierarchical Agentscited by 15
  22. 2025SparseAlign: A Fully Sparse Framework for Cooperative Object Detectioncited by 15
  23. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  24. 2023Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perceptioncited by 12
  25. 2024Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyondcited by 12
  26. 2025RENO: Real-Time Neural Compression for 3D LiDAR Point Cloudscited by 12
  27. 2023On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasurescited by 11
  28. 2024What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perceptioncited by 11
  29. 2025AirV2X: Unified Air-Ground Vehicle-to-Everything Collaborationcited by 11
  30. 2023Generating Evidential BEV Maps in Continuous Driving Spacecited by 10
  31. 2025CoopDETR: A Unified Cooperative Perception Framework for 3D Detection via Object Querycited by 9
  32. 2022VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detectioncited by 8
  33. 2023SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehiclescited by 8
  34. 2024CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environmentscited by 7
  35. 2024UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detectioncited by 7
  36. 2025CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learningcited by 7
  37. 2024RCDN: Towards Robust Camera-Insensitivity Collaborative Perception via Dynamic Feature-based 3D Neural Modelingcited by 6
  38. 2025CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systemscited by 6

50 most cited of 95 All 95 (JSON)

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  1. 2025Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
  2. 2022Decentralized cooperative perception for autonomous vehicles: Learning to value the unknown
  3. 2026CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models
  4. 2026Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning
  5. 2024Unified End-to-End V2X Cooperative Autonomous Driving
  6. 2025Collaborative Perception Datasets for Autonomous Driving: A Review
  7. 2025V2V-GoT: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Models and Graph-of-Thoughts
  8. 2024Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework