mlsanityData to 14 Oct 2026

Vehicle-to-Everything Cooperative Perception for Autonomous Driving

arXiv 2310.03525 ↗First posted 5 Oct 2023cs.CVcited by 15

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Abstract (arXiv)

Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything cooperative perception, which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. Vehicle-to-everything cooperative perception plays a crucial role in extending the perception range, increasing detection accuracy, and supporting more robust decision-making and control in complex environments. This paper provides a comprehensive survey of recent developments in vehicle-to-everything cooperative perception, introducing mathematical models that characterize the perception process under different collaboration strategies. Key techniques for enabling reliable perception sharing, such as agent selection, data alignment, and feature fusion, are examined in detail. In addition, major challenges are discussed, including differences in agents and models, uncertainty in perception outputs, and the impact of communication constraints such as transmission delay and data loss. The paper concludes by outlining promising research directions, including privacy-preserving artificial intelligence methods, collaborative intelligence, and integrated sensing frameworks to support future advancements in vehicle-to-everything cooperative perception.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 98 of its 237 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

98 of 237 references.

References · 98

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38 more
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50 most cited of 98 All 98 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 11.

15 papers.

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  1. 2025V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Drivingcited by 14
  2. 2025Generative AI for Autonomous Driving: Frontiers and Opportunitiescited by 7
  3. 2025Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspectivecited by 5
  4. 2026WhisperNet: A Scalable Solution for Bandwidth-Efficient Collaborationcited by 2
  5. 2026Infrastructure-Centric World Models: Bridging Temporal Depth and Spatial Breadth for Roadside Perceptioncited by 1
  6. 2026AFFormer: Adaptive Feature Fusion Transformer for V2X Cooperative Perception under Channel Impairmentscited by 1
  7. 2025A Survey on Deep Multi-Task Learning in Connected Autonomous Vehiclescited by 0
  8. 2026Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selectioncited by 0
  9. 2026Talk2DM: Enabling Natural Language Querying and Commonsense Reasoning for Vehicle-Road-Cloud Integrated Dynamic Maps with Large Language Modelscited by 0
  10. 2026Safety-Aligned 3D Object Detection: Single-Vehicle, Cooperative, and End-to-End Perspectivescited by 0
  11. 2026Unsupervised Multi-agent and Single-agent Perception from Cooperative Viewscited by 0
  12. 2026Adaptation-Free Heterogeneous Collaborative Perception with Unseen Agent Configurationscited by 0
3 more
  1. 2026HMS-SCP: Task-Oriented Multi-Scale Semantic Communication for V2X Cooperative Perceptioncited by 0
  2. 2026Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Drivingcited by 0
  3. 2026CoCam4D: Geometry-Aware Cooperative 4D Perception for Camera-Only Autonomous Drivingcited by 0