Conflict Judgment and Safety Assessment at Unsignalized Intersections Based on Machine Vision

Abstract

This article aims to explore an effective method for reducing vehicle collisions at unsignalized intersections. First, a monocular-binocular vision switching system is built to enable machine vision-based detection of obstacle vehicles in the left and right front directions. Then, the motion state and trajectory of each obstacle vehicle are predicted, and the intersection points of the trajectories of the obstacle vehicle and the ego vehicle are calculated. On this basis, a cross-conflict judgment model based on trajectories and collision times and a safety assessment model based on safety distance are established. Finally, the conflict judgment and safety assessment for the obstacle vehicles are simulated. The results of the simulation demonstrate that the monocular-binocular vision switching system proposed in this article can achieve a detection accuracy of 95%, a ranging accuracy of 96%, and a cross-conflict detection accuracy of 97%, while ensuring a maximum detection area, which can meet the requirements of traffic safety assurance at unsignalized intersections.