Papers › M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark
M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark
Morui Zhu, Yongqi Zhu, Yihao Zhu, Qi Chen, Deyuan Qu, Song Fu, Qing Yang
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. All resources will be made publicly available on https://github.com/zhumorui/M3CAD
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