CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization

2026-06-30 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Expert, quick

Summary

CooperScene is a new high-fidelity cooperative autonomy dataset and benchmark designed to address limitations in existing datasets for cellular vehicle-to-everything (C-V2X) communication. It features real-world C-V2X communication characterization, overcoming challenges like limited bandwidth and heterogeneous sensing. The dataset comprises diverse scenes, including intersections and highway ramps, involving three connected and autonomous vehicles (CAVs) and one infrastructure roadside unit (RSU). All agents are equipped with multi-modal sensors and commercial C-V2X radios. CooperScene provides globally consistent 3D labels at 10 Hz, totaling 344K objects across 59K frames, supported by tight synchronization, centimeter-level localization, and precise cross-modality calibration. This dataset establishes a rigorous benchmark for evaluating multi-agent scaling and actual performance in real-world deployable settings.

Key takeaway

For research scientists and engineers developing cooperative autonomy systems, CooperScene offers a critical resource for validating your models against real-world C-V2X communication challenges. You should use this benchmark to rigorously evaluate multi-agent scaling and performance, moving beyond idealized simulations. This dataset enables you to test perception, prediction, and planning algorithms under realistic bandwidth constraints and heterogeneous sensor inputs, ensuring your solutions are robust for actual deployment.

Key insights

CooperScene provides a C-V2X cooperative autonomy benchmark with real-world communication complexities, addressing existing dataset limitations.

Principles

• C-V2X enables perception beyond individual agent FOV.
• Real-world C-V2X deployment has bandwidth and sensing complexities.
• Datasets need globally consistent 3D labels and precise calibration.

In practice

• Evaluate multi-agent scaling in C-V2X systems.
• Benchmark cooperative perception and planning algorithms.
• Test C-V2X performance in diverse real-world scenarios.

Topics

• Cooperative Autonomy
• C-V2X Communication
• Autonomous Vehicles
• Multi-modal Sensing
• CooperScene Dataset
• Performance Benchmarking

Best for: Computer Vision Engineer, AI Scientist, Robotics Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.