M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark
Journal:
arXiv
Published Date:
May 10, 2025
Abstract
We introduce M$^3$CAD, a novel benchmark designed to advance research in
generic cooperative autonomous driving. M$^3$CAD 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
M$^3$CAD to support both single-vehicle and multi-vehicle autonomous driving
research, significantly broadening the scope of research in the field. To our
knowledge, M$^3$CAD 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 M$^3$CAD 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 M$^3$CAD, 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
