Unified End-to-End V2X Cooperative Autonomous Driving
On the map: End-to-end autonomous driving → Cooperative and V2X driving
Abstract (arXiv)
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.
Key facts. Cited by 1 paper 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 35 of its 52 references that are in the map. First posted on arXiv 7 May 2024.
Builds on
35 of 52 references.
References · 35
- 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,213
- 2022Planning-oriented Autonomous Drivingcited by 1,068
- 2016End to End Learning for Self-Driving Carscited by 1,000
- 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
- 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
- 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
- 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
- 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
- 2022Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Mapscited by 255
- 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
- 2020When2com: Multi-Agent Perception via Communication Graph Groupingcited by 157
- 2022V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Drivingcited by 157
23 more
- 2020Who2com: Collaborative Perception via Learnable Handshake Communicationcited by 127
- 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
- 2022Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipecited by 122
- 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
- 2019Multimodal End-to-End Autonomous Drivingcited by 110
- 2019A Survey of Deep Learning Applications to Autonomous Vehicle Controlcited by 101
- 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
- 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 99
- 2023Collaborative Perception in Autonomous Driving: Methods, Datasets and Challengescited by 87
- 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
- 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 73
- 2023DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Drivingcited by 73
- 2023FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Drivingcited by 67
- 2021A Tutorial on 5G NR V2X Communicationscited by 29
- 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
- 2018End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perceptioncited by 27
- 2023Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Predictioncited by 25
- 2020An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviorscited by 16
- 2020A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Developmentcited by 11
- 2023A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehiclescited by 9
- 2019TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Datacited by 8
- 2023Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Drivingcited by 3
- 2022A Comprehensive Survey on the Applications of Blockchain for Securing Vehicular Networkscited by 1
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Citing papers per year: 2025: 1.
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- 2025Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
- 2025UniMM-V2X: MoE-Enhanced Multi-Level Fusion for End-to-End Cooperative Autonomous Driving
- 2024V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models
- 2026Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving
- 2024Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
- 2025CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction
- 2026OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving