A Roadside LiDAR and HD-Map Framework for Early Wrong-Way Driving Detection

Wrong-way driving (WWD) is a rare but highly dangerous traffic event that can lead to severe head-on collisions. This study proposes a LiDAR-based infrastructure system for early WWD detection using roadside sensing and high-definition map semantics. The proposed framework integrates object detection, multi-object tracking, and lane-level directional reasoning to determine whether observed vehicle motion is consistent with the intended lane direction. To improve robustness in complex geometries, the system combines consecutive-frame validation with event-level heading analysis relative to the intended lane direction. The method was evaluated under both daytime and nighttime operating conditions using condition-specific ground-truth event annotations. In the staged daytime and nighttime validation, the framework detected all 14 daytime and 22 nighttime annotated WWD events, with no false-positive (FP) event observed among 67 daytime and 88 nighttime normal vehicle passes. The observed FP event rate was 0 events/h in both conditions, and the average time to detection was −0.36 s in daytime and −0.297 s at nighttime, indicating that the system generally identified WWD behavior before the annotated event onset. Under the evaluated staged surrogate-site conditions, these results demonstrate the feasibility of the proposed framework for event-level early WWD detection. Long-duration operation, downstream warning actuation, and validation across diverse roadway geometries and environmental conditions remain to be evaluated.