构建首个面向多任务协同自动驾驶的综合基准，支持跨车辆协作研究。
M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark
- 设计多车多模态数据集，含204段序列、3万帧，融合激光雷达与视觉数据。
- 提出自适应多级融合方法，在带宽受限下仍保持高感知精度。
- 适合研究协同感知、路径规划等方向的开发者与研究人员使用。
我们提出了M³CAD，一个全面的基准，旨在推动通用协同自动驾驶研究。该基准包含204个序列，共30,000帧，每段序列涵盖多车及多种传感器数据，如激光雷达点云、RGB图像和GPS/IMU信息，支持目标检测与跟踪、建图、运动预测、占用预测和路径规划等多种自动驾驶任务。丰富的多模态设置使M³CAD可支持单车与多车协同研究。据我们所知，M³CAD是目前最完整的专为协同、多任务自动驾驶设计的基准。为验证其有效性，我们使用M³CAD评估了当前最先进的单车与协同驾驶方案，并建立了基线性能。由于多数现有协同感知方法聚焦特征融合但忽略网络带宽限制，我们提出一种新的多级融合策略，根据实时网络条件自适应平衡通信效率与感知精度。我们已公开发布M³CAD、基线模型及评估结果，以支持鲁棒协同自动驾驶系统的发展，所有资源将通过GitHub公开提供。
原文摘要 · Abstract (English)
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
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