Risk Identification and Conflict Prediction from Videos Based on TTC-ML of a Multi-Lane Weaving Area

Abstract

Crash risk identification and prediction are expected to play an important role in traffic accident prevention. However, most of the existing studies focus only on highways, not on multi-lane weaving areas. In this paper, a potential collision risk identification and conflict prediction model based on extending Time-to-Collision-Machine Learning (TTC-ML) for multi-lane weaving zone was proposed. The model can accurately learn various features, such as vehicle operation characteristics, risk and conflict distributions, and physical zoning characteristics in the weaving area. Specifically, TTC was used to capture the collision risk severity, and ML extracted vehicle trajectory features. After normalizing and dimensionality reduction of the vehicle trajectory dataset, Naive Bayes, Logistic Regression, and Gradient Boosting Decision Tree (GBDT) models were selected for traffic conflict prediction, and the experiments showed that the GBDT model outperforms two remaining models in terms of prediction accuracy, precision, false-positive rate (FPR) and Area Under Curve (AUC). The research findings of this paper help traffic management departments develop and optimize traffic control schemes, which can be applied to Intelligent Vehicle Infrastructure Cooperative Systems (IVICS) dynamic warning.

Research on Collision Avoidance Systems for Intelligent Vehicles Considering Driver Collision Avoidance Behaviour

“…Since the steering behaviour of drivers is more aggressive during the actual lane change collision avoidance process in case of emergency, the cases with lateral acceleration greater than 0.4 g were filtered out to obtain 322 sets of data for US-101 roads and 326 sets of data for I-80 roads for a total of 648 cases. It is clear from the literature [24] that the TTC (time of own collision with the front) model is effective and simple to calculate as it can be used to identify collision hazard levels. Therefore, according to the model, the TTC > 3 s is set as no collision hazard, 2 s < TTC < 3 s as primary collision hazard, 1 s < TTC < 2 s as secondary collision hazard and TTC < 1 s as tertiary collision hazard.…”

Automated Vehicle to Vehicle Conflict Analysis at Signalized Intersections by Camera and LiDAR Sensor Fusion

“…TTC is one of the most-used SSMs for traffic safety assessment in versatile scenarios including, but not limited to, freeway and urban intersections. In recent research, video-based vehicle trajectories are used to compute TTC for potential conflict risk identification for multi-lane weaving areas ( 10 ). Researchers have also developed a traffic danger recognition model using traffic surveillance cameras ( 11 ).…”