MM-Drive: Multi-Modal Dataset and Benchmark for Diverse Driving Scenarios with Stereo Event-RGB-Thermal Cameras, LiDAR, and 4D Radar

We present MM-Drive, a driving dataset that incorporates stereo event, RGB, and thermal cameras together with 4D radar and dual LiDAR, collected across diverse weather and illumination conditions. The dataset provides precise 2D and 3D bounding boxes with track IDs and ego vehicle odometry, enabling fair comparisons within and across sensor combinations. It is designed to alleviate data scarcity for novel sensors such as event cameras and 4D radar and to support systematic studies of their behavior. We establish unified 3D and 2D benchmarks and propose a fusion framework that integrates sensor-specific cues into a unified feature space, improving 3D detection robustness under varied weather and lighting.