Biblio

Turning through oncoming traffic at unsignalized intersections can lead to safety-critical situations contributing to 7.4% of all non-severe vehicle crashes. One of the main reasons for these crashes are human errors in the form of incorrect estimation of the gap size with respect to the Principle Other Vehicle (POV). Vehicle-to-vehicle (V2V) technology promises to increase safety in various traffic situations. V2V infrastructure combined with further integration of sensor technology and human intention prediction could help reduce the frequency of these safety-critical situations by predicting dangerous turning manoeuvres in advance, thus, allowing the POV to prepare an appropriate reaction. We performed a driving simulator study to investigate turning decisions at unsignalized intersections. Over the course of the experiments, we recorded over 5000 turning decisions with respect to different gap sizes. Afterwards, the participants filled out a questionnaire featuring demographic and driving style related items. The behavioural and questionnaire data was then used to fit a Bayesian Network model to predict the turning intention of the subject vehicle. We evaluate the model and present the results of a feature importance analysis. The model is able to correctly predict the turning intention with an accuracy of 74%. Furthermore, the feature importance analysis indicates that user specific information is a valuable contribution to the model. We discuss how a working turning intension prediction could reduce the number of safety-critical situations.