Before the widespread use of autonomous vehicles, it is needed to reveal the travelers’
intentions and behavior patterns; with the conclusions gained appropriate response
can be provided by the vehicle in a conflict situation. This paper focuses on vehicle-pedestrian
conflict points. The aim was to elaborate a method to prevent pedestrians from being
hit by an autonomous vehicle. Typical pedestrian movements have been identified by
an on-site measurement and a questionnaire survey. Pedestrian crossing patterns were
categorized based on the onsite measurement, whereas general habits and feelings behind
a crossing were identified by the questionnaire survey. We found that the typical
approach angle of a crossing point is 90 degrees. Moreover, the movements approaching
a crossing point are
strongly influenced by the environment (e.g. location of public transportation stop).
Additionally, crossing behavior is strongly influenced by gender. The results support
the prediction of pedestrians’ position, which can be used in the software development
of autonomous vehicles.