# Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition arXiv: https://arxiv.org/abs/2507.21610 ยท HTML: https://mlsanity.com/paper/2507.21610/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. - Builds on 62 of its 99 references that are in the map. - First posted on arXiv 29 Jul 2025. ## Placed in - End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/) ## Builds on (most cited first, 30 of 62) - [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019) - [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022) - [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019) - [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023) - [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022) - [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021) - [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023) - [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022) - [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](https://mlsanity.com/paper/2406.15349/) (2024) - [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022) - [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://mlsanity.com/paper/2311.17918/) (2023) - [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022) - [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024) - [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022) - [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023) - [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023) - [VLP: Vision Language Planning for Autonomous Driving](https://mlsanity.com/paper/2401.05577/) (2024) - [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023) - [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022) - [A Survey on Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2506.24044/) (2025) - [TUMTraf V2X Cooperative Perception Dataset](https://mlsanity.com/paper/2403.01316/) (2024) - [V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception](https://mlsanity.com/paper/2403.16034/) (2024) - [V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models](https://mlsanity.com/paper/2408.09251/) (2024) - [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025) - [End-to-End Autonomous Driving through V2X Cooperation](https://mlsanity.com/paper/2404.00717/) (2024) - [RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception](https://mlsanity.com/paper/2403.10145/) (2024) - [AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving](https://mlsanity.com/paper/2412.15206/) (2024) - [DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection](https://mlsanity.com/paper/2312.15742/) (2023) ## Built on by (most cited first, 9 of 9) - [DriveE2E: Closed-Loop Benchmark for End-to-End Autonomous Driving through Real-to-Simulation](https://mlsanity.com/paper/2509.23922/) (2025) - [DriveVGGT: Calibration-Constrained Visual Geometry Transformers for Multi-Camera Autonomous Driving](https://mlsanity.com/paper/2511.22264/) (2025) - [SparseCoop: Cooperative Perception with Kinematic-Grounded Queries](https://mlsanity.com/paper/2512.06838/) (2025) - [SafeCoop: Unravelling Full Stack Safety in Agentic Collaborative Driving](https://mlsanity.com/paper/2510.18123/) (2025) - [Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception](https://mlsanity.com/paper/2604.09206/) (2026) - [Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2606.04492/) (2026) - [Rethinking Air-Ground Collaboration: A Progressive Cross-Task Benchmark and Socialized Learning Framework](https://mlsanity.com/paper/2606.18841/) (2026) - [HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework](https://mlsanity.com/paper/2607.26283/) (2026) - [FFBL-Coop: Association-Decoupled Cooperative 3D Multi-Object Tracking](https://mlsanity.com/paper/2610.01750/) (2026) ## Abstract (arXiv) With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety by providing visibility beyond the line of sight. However, integrating multi-source sensor data from both ego-vehicles and infrastructure under real-world constraints, such as limited communication bandwidth and dynamic environments, presents significant technical challenges. To facilitate research in this area, we organized the End-to-End Autonomous Driving through V2X Cooperation Challenge, which features two tracks: cooperative temporal perception and cooperative end-to-end planning. Built on the UniV2X framework and the V2X-Seq-SPD dataset, the challenge attracted participation from over 30 teams worldwide and established a unified benchmark for evaluating cooperative driving systems. This paper describes the design and outcomes of the challenge, highlights key research problems including bandwidth-aware fusion, robust multi-agent planning, and heterogeneous sensor integration, and analyzes emerging technical trends among top-performing solutions. By addressing practical constraints in communication and data fusion, the challenge contributes to the development of scalable and reliable V2X-cooperative autonomous driving systems.