@inproceedings{Aizono2026,
title = {RealSim-CP: A High-Fidelity Multimodal Cooperative Perception Dataset Bridging the Simulator–Real World Gap},
author = {Yuji Aizono and Ehsan Javanmardi and Fardin Ayar and Mahdi Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/tlab-wide/realsim-cp
https://tlab-wide.github.io/realsim-cp/},
year = {2026},
date = {2026-06-09},
urldate = {2026-06-09},
booktitle = {2026 IEEE 103rd Vehicular Technology Conference (VTC2026-Spring)},
address = {Nice, France},
abstract = {Cooperative perception, which enables vehicles to share sensory information with other vehicles and roadside infrastructure, is essential for advancing autonomous driving beyond single-vehicle limitations. However, existing cooperative perception datasets suffer from two critical limitations: the lack of representation of Japanese traffic environments with their unique characteristics (e.g., left-hand traffic, distinctive vehicle types, and region-specific infrastructure), and the high cost of real-world data collection that constrains dataset scale and diversity.
To address these challenges, we present RealSim-CP, a novel cooperative perception dataset generated using the Driving Intelligence Validation Platform (DIVP), a physics-based simulation system that employs ray tracing and electromagnetic-wave modeling to produce highly realistic sensor data comparable to real-world quality. Leveraging DIVP’s high-fidelity simulation capabilities, we efficiently generate a large-scale dataset comprising synchronized multimodal data—camera images and LiDAR point clouds—from multiple cooperative agents, including vehicles and roadside units. The dataset covers three urban maps representing Tokyo regions (Aomi, Odaiba, and the Shutoko Expressway) under diverse environmental conditions, including clear daytime, rainy daytime, and clear nighttime scenarios.
All data are provided in the standardized OpenLABEL format with annotations for 12 object classes, totaling 140k images and 30k point clouds. We further evaluate RealSim-CP using CoopDet3D, a state-of-the-art multimodal cooperative 3D object detection framework, demonstrating the effectiveness of the dataset for advanced cooperative perception research. These results indicate that high-fidelity simulation can effectively bridge the gap between simulation and real-world deployment while significantly reducing data collection costs. RealSim-CP provides the first region-specific cooperative perception dataset tailored to Japanese traffic environments.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Cooperative perception, which enables vehicles to share sensory information with other vehicles and roadside infrastructure, is essential for advancing autonomous driving beyond single-vehicle limitations. However, existing cooperative perception datasets suffer from two critical limitations: the lack of representation of Japanese traffic environments with their unique characteristics (e.g., left-hand traffic, distinctive vehicle types, and region-specific infrastructure), and the high cost of real-world data collection that constrains dataset scale and diversity.
To address these challenges, we present RealSim-CP, a novel cooperative perception dataset generated using the Driving Intelligence Validation Platform (DIVP), a physics-based simulation system that employs ray tracing and electromagnetic-wave modeling to produce highly realistic sensor data comparable to real-world quality. Leveraging DIVP’s high-fidelity simulation capabilities, we efficiently generate a large-scale dataset comprising synchronized multimodal data—camera images and LiDAR point clouds—from multiple cooperative agents, including vehicles and roadside units. The dataset covers three urban maps representing Tokyo regions (Aomi, Odaiba, and the Shutoko Expressway) under diverse environmental conditions, including clear daytime, rainy daytime, and clear nighttime scenarios.
All data are provided in the standardized OpenLABEL format with annotations for 12 object classes, totaling 140k images and 30k point clouds. We further evaluate RealSim-CP using CoopDet3D, a state-of-the-art multimodal cooperative 3D object detection framework, demonstrating the effectiveness of the dataset for advanced cooperative perception research. These results indicate that high-fidelity simulation can effectively bridge the gap between simulation and real-world deployment while significantly reducing data collection costs. RealSim-CP provides the first region-specific cooperative perception dataset tailored to Japanese traffic environments.
