Aerial Dataset for China Congested Highway & Expressway
A drone-based aerial naturalistic driving dataset capturing congested traffic on Chinese highway expressways — rich car-following, lane-change, and forced-merge interactions under constrained traffic flow.
Drag the slider to compare raw drone video with standardized trajectories on the HD map — sample case from AD4CHE.
AD4CHE focuses on the safety-critical part of expressway driving — congested traffic — where safety-critical interactions concentrate and conventional in-vehicle datasets struggle to capture clean trajectories.
High-precision vehicle trajectories captured from a top-down drone perspective. Dense car-following, frequent lane changes, and forced merges that ground-level sensors miss.
Recordings target stop-and-go waves, short-headway intrusions, and constrained-gap merging — the long-tail traffic states where most safety-critical interactions occur.
Used by 400+ academic and industrial institutions for ADAS calibration, motion-prediction benchmarks, scenario extraction, and human-like driving model training.
Confirmed metrics from the AD4CHE release. Refer to the IEEE T-IV paper for the full breakdown by site and recording.
Each release ships with rich kinematic and contextual fields, ready for direct scenario analysis.
Please cite the AD4CHE paper in any publication that uses this dataset.
"The AD4CHE Dataset and Its Application in Typical Congestion Scenarios of Traffic Jam Pilot Systems"
Free for non-commercial academic research. We review every request manually and typically respond within 5 business days.
Application is submitted via our Feishu form. We typically respond within 5 business days.
Companion open datasets from the lab and partner institutions, covering different road types and traffic agents.