Whether autonomous driving can effectively handle challenging scenarios such as bad weather and complex traffic environments is still in doubt. One of the critical difficulty is that the single-agent perception is hard to obtain the complementary perceptual information around the multi-condition scenes, such as meeting occlusion. To investigate the advantages of collaborative perception in high-risky driving scenarios, we constructed a multiple challenging condition dataset for large-range vehicle-Infrastructure cooperative perception, called V2XScenes, which include seven typical multi-modal sensor layouts at successive road section. Particularly, each selected scene is labeled with specific condition description, and we provide the unique global object tracking numbers across the entire road section and sequential frames to ensure consistency. Comprehensive cooperative perception benchmarks of 3D object detection and tracking are provided, the quantitative results based on the state-of-the-art demonstrate the effectiveness of collaborative perception facing corner condition.
