We introduce M3CAD, a comprehensive benchmark designed to advance research in generic cooperative autonomous driving. M3CAD comprises 204 sequences with 30,000 frames. Each sequence includes data from multiple vehicles and different types of sensors, e.g., LiDAR point clouds, RGB images, and GPS/IMU, supporting a variety of autonomous driving tasks, including object detection and tracking, mapping, motion forecasting, occupancy prediction, and path planning. This rich multimodal setup enables M3CAD to support both single-vehicle and multi-vehicle cooperative autonomous driving research. To the best of our knowledge, M3CAD is the most complete benchmark specifically designed for cooperative, multi-task autonomous driving research. To test its effectiveness, we use M3CAD to evaluate both state-of-the-art single-vehicle and cooperative driving solutions, setting baseline performance results. Since most existing cooperative perception methods focus on merging features but often ignore network bandwidth requirements, we propose a new multi-level fusion approach which adaptively balances communication efficiency and perception accuracy based on the current network conditions. We release M3CAD, along with the baseline models and evaluation results, to support the development of robust cooperative autonomous driving systems. All resources will be made publicly available on https://github.com/zhumorui/M3CAD
@article{arxiv.2505.06746,
title = {M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark},
author = {Morui Zhu and Yongqi Zhu and Yihao Zhu and Qi Chen and Deyuan Qu and Song Fu and Qing Yang},
journal= {arXiv preprint arXiv:2505.06746},
year = {2026}
}