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

2026-06-30

AI Summary

Existing autonomous driving datasets inadequately capture critical real-world deployment challenges such as communication bandwidth constraints, heterogeneous perception modalities, and multi-agent scalability. To address this gap, this work presents a high-fidelity, multimodal cooperative perception dataset collected in diverse scenarios—including intersections, on-ramps, and parking lots—featuring three connected autonomous vehicles and one roadside unit, all equipped with commercial C-V2X devices and multimodal sensors. The dataset provides globally consistent 3D annotations at 10 Hz and, for the first time in a real-world C-V2X setting, achieves tight multi-agent synchronization, centimeter-level positioning accuracy, and precise cross-modal calibration while adhering to 3GPP communication standards. Comprising 59K frames and 344K object annotations, it establishes a realistic benchmark for deployable multi-agent cooperative perception, prediction, and planning.

Abstract

Cellular vehicle-to-everything (C-V2X) enables cooperative perception, prediction, and planning beyond the field of view of individual agents. However, existing datasets often overlook the complexities of real-world deployment, such as limited communication bandwidth and its dynamics, heterogeneous sensing modalities, and scalability beyond a single cooperative partner. In this paper, we introduce CooperScene, a high-fidelity cooperative autonomy dataset with real-world C-V2X communication characterization. The dataset is organized into diverse scenes, including intersections, highway ramps, and parking lots. These scenes involve three connected and autonomous vehicles (CAVs) and one infrastructure roadside unit (RSU), all equipped with multi-modal sensors and commercial off-the-shelf C-V2X communication radios. All scenes are annotated with globally consistent 3D labels at 10 Hz, totaling 344K objects across 59K frames, underpinned by tight sensor- and agent-synchronization, centimeter-level localization and spatial alignment, precise cross-modality calibration, and 3GPP-standard-compliant C-V2X communication. CooperScene establishes a rigorous benchmark for evaluating multi-agent scaling and actual performance in real-world deployable settings. Project website for data and benchmark: https://cisl.ucr.edu/CooperScene

Problem

• C-V2X communication
• cooperative autonomy
• multi-modal sensing
• scalability
• real-world deployment

Innovation

• C-V2X communication
• multi-modal perception
• cooperative autonomy
• real-world benchmark
• heterogeneous sensing