An Algorithm for Spatio-Temporal Trajectory Conflict Risk Identification in Intersections Considering Lateral Vehicle Movement

Vehicle trajectories from different directions within intersections intersect and create conflicts on a two-dimensional plane. Compared to highways, the lateral movement of vehicles within intersections is irregular and difficult to predict. This risk from lateral trajectories extends to the surrounding areas, impacting other vehicles within the intersection and subsequently reducing both safety and efficiency. To address this issue, a framework for identifying the conflict risk of vehicle trajectory based on lateral movement at intersections is proposed in this paper. Firstly, the concept of virtual lanes is employed to extract abnormal trajectories of lateral vehicle movement within intersections. Subsequently, a spatio-temporal hexahedral conflict detection algorithm, based on the vehicle border, is developed. Finally, the causes of abnormal lateral trajectories within intersections and their relationship with conflict events are discussed in detail, and hotspot areas of vehicle safety risks within intersections are identified. To validate the proposed model, typical intersection vehicle trajectory data captured by uncrewed aerial vehicles is utilized. The research results indicate that abnormal lateral movements of vehicles within intersections are common and pose significant risks to neighboring vehicles. The data reveal a specific pattern in the abnormal trajectory and trajectory density at intersections, with abnormal lateral trajectories of left-turning vehicles accounting for as much as 95.02% of cases. Furthermore, abnormal lateral movement in risky trajectories accounted for up to 86.6%. The proposed method in this paper provides support for intersection safety assessments, abnormal lateral trajectory warning systems, and real-time risk calculations for connected autonomous vehicles and Car2Car communication.