Modeling Driver Behavior at Roundabouts: Results from a Field Study.

Abstract

Driving behavior prediction at roundabouts is an important challenge to improve driving safety by supporting drivers with intelligent assistance systems. To predict the driving behavior efficiently steering wheel status was proven to have robust predictability based on a Support Vector Machine algorithm. Previous research has not considered potential effects of surrounding traffic on driving behavior, but that consideration can certainly improve the prediction results. Therefore, this study investigated how different surrounding cyclists impact driving behavior of an ego car. A simulator study was conducted to collect driving behavior data of ego car drivers in the scenarios with different surrounding cyclist position settings. The impact of the surrounding cyclists on the ego driver behavior was found: When there were surrounding cyclists, the recognition rate of ego driver behavior patterns reached 100% later than when there was no surrounding traffic. In conclusion, driving behavior pattern recognition at roundabouts is impacted by surrounding cyclists, and the impact can be expressed in a quantitative way.