Online risk estimation of critical and non-critical interactions between right-turning motorists and crossing cyclists by a decision tree

Abstract

One of the most critical situations in urban areas is when motorists turn right in an intersection and cyclists cross the road. Many of those crashes result in severe consequences for cyclists. In order to increase the safety of cyclists, especially in the case of conflicts with right-turning vehicles, an online infrastructure-based assistance system may be a promising solution warning drivers and cyclists when a conflict or crash is predicted. By means of automated video traffic detection, the resulting trajectories of road users can be analysed and a warning can be sent to vehicles and cyclists equipped with vehicle-to-anything communication (V2X) when a high risk is estimated. An approach for online risk estimation was developed combining the surrogate measure of safety (SMoS) gap time (GT) with trajectory prediction-based estimates of the time-to-arrival (TTA) or distance to conflict point (DCp) and velocity (v). A decision tree as classifier of risk levels based on the previous named risk features was trained to model the risks perceived by humans. Expert ratings of traffic conflict scenes were used to build a model, apply the model, and improve it in the field. The warning system was evaluated by test drives in real traffic at the urban AIM Research Intersection in Braunschweig, Germany. In general, the system warned reliably. In approximately 67% of the trials, it was assessed as helpful.