An Automatic Conflict Detection Framework for Urban Intersections Based on an Improved Time Difference to Collision Indicator

Abstract

Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately.

Image-based Conflict Detection with Convolutional Neural Network under Weather Uncertainty

“…Recent advances in deep learning models (used mostly in image processing) showed their effectiveness in detecting conflicts amongst the vehicular traffic. In particular, the convolutional neural network (CNN) model has been used in the object tracking of vehicles for conflict detection [16]. Adopting a deep learning approach in the ATM domain would not be the first of its kind per se.…”