Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition

• URL: http://arxiv.org/abs/2507.21610v1
• Date: Tue, 29 Jul 2025 09:06:40 GMT
• Title: Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
• Authors: Ruiyang Hao, Haibao Yu, Jiaru Zhong, Chuanye Wang, Jiahao Wang, Yiming Kan, Wenxian Yang, Siqi Fan, Huilin Yin, Jianing Qiu, Yao Mu, Jiankai Sun, Li Chen, Walter Zimmer, Dandan Zhang, Shanghang Zhang, Mac Schwager, Wei Huang, Xiaobo Zhang, Ping Luo, Zaiqing Nie,
• Abstract summary: Vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety.<n>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.<n>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.
• Score: 57.698383942708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework [62.47416496137193]
  We propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop.<n>The architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime.
  arXiv  Detail & Related papers  (2025-03-06T07:36:06Z)
• Unified End-to-End V2X Cooperative Autonomous Driving [21.631099800753795]
  UniE2EV2X is 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. We implement the UniE2EV2X framework on the challenging DeepAccident, a simulation dataset designed for V2X cooperative driving.
  arXiv  Detail & Related papers  (2024-05-07T03:01:40Z)
• Collaborative Perception Datasets in Autonomous Driving: A Survey [0.0]
  The paper systematically analyzes a variety of datasets, comparing them based on aspects such as diversity, sensor setup, quality, public availability, and their applicability to downstream tasks. The importance of addressing privacy and security concerns in the development of datasets is emphasized, regarding data sharing and dataset creation.
  arXiv  Detail & Related papers  (2024-04-22T09:36:17Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges [32.11627955649814]
  Vehicle-to-everything (V2X) cooperative perception (CP) allows vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. V2X CP is crucial for extending perception range, improving accuracy, and strengthening the decision-making and control capabilities of autonomous vehicles in complex environments. This paper provides a comprehensive survey of recent advances in V2X CP, introducing mathematical models of CP processes across various collaboration strategies.
  arXiv  Detail & Related papers  (2023-10-05T13:19:48Z)
• V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration [4.166623313248682]
  This paper presents a LiDAR-based end-to-end autonomous driving method with Vehicle-to-Everything (V2X) communication integration. The proposed method aims to handle imperfect partial observations by fusing the onboard LiDAR sensor and V2X communication data.
  arXiv  Detail & Related papers  (2023-09-26T20:26:03Z)
• End-to-end Autonomous Driving: Challenges and Frontiers [45.391430626264764]
  We provide a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. We delve into several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models, amongst others. We discuss current advancements in foundation models and visual pre-training, as well as how to incorporate these techniques within the end-to-end driving framework.
  arXiv  Detail & Related papers  (2023-06-29T14:17:24Z)
• V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer [58.71845618090022]
  We build a holistic attention model, namely V2X-ViT, to fuse information across on-road agents. V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention. To validate our approach, we create a large-scale V2X perception dataset.
  arXiv  Detail & Related papers  (2022-03-20T20:18:25Z)
• V2X-Sim: A Virtual Collaborative Perception Dataset for Autonomous Driving [26.961213523096948]
  Vehicle-to-everything (V2X) denotes the collaboration between a vehicle and any entity in its surrounding. We present the V2X-Sim dataset, the first public large-scale collaborative perception dataset in autonomous driving.
  arXiv  Detail & Related papers  (2022-02-17T05:14:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.