Surrogate safety measures in the form of conflict indicators are
indispensable components of the proactive traffic safety toolbox. Conflict
indicators can be classified into past-trajectory-based conflicts and
predicted-trajectory-based conflicts. While the calculation of the former class
of conflicts is deterministic and unambiguous, the latter category is computed
using predicted vehicle trajectories and is thus more stochastic. Consequently,
the accuracy of prediction-based conflicts is contingent on the accuracy of the
utilized trajectory prediction algorithm. Trajectory prediction can be a
challenging task, particularly at intersections where vehicle maneuvers are
diverse. Furthermore, due to limitations relating to the road user trajectory
extraction pipelines, accurate geometric representation of vehicles during
conflict analysis is a challenging task. Misrepresented geometries distort the
real distances between vehicles under observation. In this research, a
prediction-based conflict identification methodology was proposed. A
sequence-to-sequence Recurrent Neural Network was developed to sequentially
predict future vehicle trajectories for up to 3 seconds ahead. Furthermore, the
proposed network was trained using the CitySim Dataset to forecast both future
vehicle positions and headings to facilitate the prediction of future bounding
boxes, thus maintaining accurate vehicle geometric representations. It was
experimentally determined that the proposed method outperformed frequently used
trajectory prediction models for conflict analysis at intersections. A
comparison between Time-to-Collision (TTC) conflict identification using
vehicle bounding boxes versus the commonly used vehicle center points for
geometric representation was conducted. Compared to the bounding box method,
the center point approach often failed to identify TTC conflicts or
underestimated their severity.

提出一种基于预测的冲突识别方法，使用递归神经网络进行车辆轨迹预测，并利用 CitySim 数据集训练模型以预测车辆未来路径和头部位置，从而更准确地维护车辆几何表示。实验发现，该方法在交叉口冲突分析中表现优于常用的轨迹预测模型和车辆几何表示方法。