We introduce M3CAD, a novel benchmark designed to advance research in generic cooperative autonomous driving. M3CAD comprises 204 sequences with 30k frames, spanning a diverse range of cooperative driving scenarios. Each sequence includes multiple vehicles and sensing modalities, 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 autonomous driving research, significantly broadening the scope of research in the field. To our knowledge, M3CAD is the most comprehensive benchmark specifically tailored for cooperative multi-task autonomous driving research. We evaluate the state-of-the-art end-to-end solution on M3CAD to establish baseline performance. To foster cooperative autonomous driving research, we also propose E2EC, a simple yet effective framework for cooperative driving solution that leverages inter-vehicle shared information for improved path planning. We release M3CAD, along with our baseline models and evaluation results, to support the development of robust cooperative autonomous driving systems.
M3CAD is comprehensively compared with existing autonomous driving benchmarks. The table highlights differences in Cooperation Type (CT), and supported tasks such as Object Detection (OD), Object Tracking (OT), Mapping (MP), Motion Forecasting (MF), Occupancy Prediction (OCC), and Path Planning (PP).
We evaluate the UniAD model on M3CAD, comparing its performance across different stages and tasks.
Comparison of UniAD's first stage performance on different benchmarks. S.:Single-vehicle tasks, C.: Cooperative tasks.
Comparison of UniAD's second stage performance on different benchmarks. S.:Single-vehicle tasks, C.: Cooperative tasks.
This section introduces the End-to-End (E2E) framework used in our project.
This section visually compares the performance of various autonomous driving tasks under non-cooperative and cooperative settings. Use the buttons below to switch between different tasks (Detection, Mapping, Motion Forecasting, Planning, Occupancy) and observe the improvements achieved through cooperation.
@misc{zhu2025m3cad,
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},
year={2025},
eprint={2505.06746},
archivePrefix={arXiv},
primaryClass={cs.RO}
}