M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917323523227648 |
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| author | Zhu, Morui Zhu, Yongqi Zhu, Yihao Chen, Qi Qu, Deyuan Fu, Song Yang, Qing |
| author_facet | Zhu, Morui Zhu, Yongqi Zhu, Yihao Chen, Qi Qu, Deyuan Fu, Song Yang, Qing |
| contents | We introduce M$^3$CAD, a comprehensive benchmark designed to advance research in generic cooperative autonomous driving. M$^3$CAD 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 M$^3$CAD to support both single-vehicle and multi-vehicle cooperative autonomous driving research. To the best of our knowledge, M$^3$CAD is the most complete benchmark specifically designed for cooperative, multi-task autonomous driving research. To test its effectiveness, we use M$^3$CAD 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 M$^3$CAD, 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06746 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark Zhu, Morui Zhu, Yongqi Zhu, Yihao Chen, Qi Qu, Deyuan Fu, Song Yang, Qing Robotics Computer Vision and Pattern Recognition I.2.10; I.2.9 We introduce M$^3$CAD, a comprehensive benchmark designed to advance research in generic cooperative autonomous driving. M$^3$CAD 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 M$^3$CAD to support both single-vehicle and multi-vehicle cooperative autonomous driving research. To the best of our knowledge, M$^3$CAD is the most complete benchmark specifically designed for cooperative, multi-task autonomous driving research. To test its effectiveness, we use M$^3$CAD 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 M$^3$CAD, 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 |
| title | M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark |
| topic | Robotics Computer Vision and Pattern Recognition I.2.10; I.2.9 |
| url | https://arxiv.org/abs/2505.06746 |