This dataset focuses on multi-target detection at traffic accident scenes and provides rich image data to support the research of intelligent transportation systems.

This dataset is used for identifying parking or slow-moving behaviors in tunnels.

This dataset provides high-quality target detection data for subway entrance safety monitoring, aiding in traffic safety management.

This dataset focuses on multi-target (vehicles, traffic police, environment) recognition at night, providing data support for traffic safety.

Connect webpage text, evidence paragraphs, and structured verification labels to help models assess the temporal and scenario boundaries of transportation forecasts.

Train and evaluate topology-based reasoning with connectivity questions grounded in closed-campus HD maps.

This dataset is used to detect stall and obstruction behaviors within transportation hub stations to improve traffic management efficiency.

This dataset is used to identify anomalies in the inclination structure of trucks to reduce accident risks.

This dataset focuses on semantic segmentation of vehicle collision damage in tunnel environments, aiming to improve the efficiency and accuracy of traffic accident analysis.

This dataset provides high-quality images for vehicle collision accidents, supporting research on target detection and damage assessment.

This dataset provides high-quality images of urban traffic construction scenes to support object detection research.

This is an image dataset for detecting road speed bump damage.

This dataset focuses on the automatic recognition of tunnel traffic control signs, promoting the process of traffic intelligence.

Ground dry bulk port and terminal operations answers in source webpages and evidence passages for more traceable RAG systems.

This dataset focuses on license plate recognition and vehicle attribute analysis in underground parking lots, supporting object detection tasks.

This dataset focuses on the automatic recognition of illegal parking, providing rich annotated data.

This dataset is for identifying road blockages and illegal parking, supporting the development of smart traffic systems.

This dataset focuses on illegal parking detection on urban roads, providing abundant labeled images and detailed information.

Evaluate how well models understand cross-layer positions, alignment, and containment in HD maps featuring lane markings and road boundaries.

This dataset focuses on the identification and analysis of rear-end collisions in tunnels to enhance traffic safety levels.