Model-Based Prediction of Two-Wheelers

The breakthrough of intelligent vehicles will also be determined by the safety gain they provide. In order to perform accident avoiding reactions at the earliest point in time possible, predictions about the future behavior of other traffic participants are needed. The most exposed share of traffic participants regarding this issue are single-track two-wheelers (1T2W): they share the road with cars and trucks but are not as agile due to their kinematics. Furthermore, they are faster than pedestrians but comparably vulnerable as those. In order to guarantee their safety, we make use of their movement restricting kinematics. We simulate three typical classes of 1T2W under conservative assumptions about their agility in order to generate a spatial region in which they have to be due to physics after a fixed prediction horizon of up to 1.5 seconds. The proposed approach was verified in experiments with real high-dynamic driving maneuvers.