In this work, a framework for motion prediction of vehicles and safety assessment of traffic
scenes is presented. The developed framework can be used for driver assistant systems as …

Due to the large number and the high variability of possible traffic situations, intersections
are among the most accident-prone spots in inner-city traffic. To reliably assist the driving …

Operation in a real world traffic requires the ability to plan motion in complex environments
(multiple moving participants) from autonomous vehicles. Navigation through such …

Estimating and predicting traffic situations over time is an essential capability for
sophisticated driver assistance systems and autonomous driving. When longer prediction …

Microscopic modeling of driving behavior is the basis for traffic design and traffic simulation
studies and can be applied to automated driving systems to provide human-like decision …

State of the art emergency brake assistant systems solely based on sensor measurements
reduced the number of traffic accidents and casualties drastically in recent years. In order to …

This paper presents a general model for lane-change decision and dynamic lane change
behavior based on the Foresighted Driver Model. Differently to previous models, it does not …

Current driving behaviour models are designed for specific scenarios, such as curve driving,
obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse …

We present a novel approach for vehicle-pedestrian collision risk analysis that incorporates
mutual situational awareness, a degree of potential motion coupling and the spatial layout of …

We present a novel approach for risk-aware planning with human agents in multi-agent
traffic scenarios. Our approach takes into account the wide range of human driver behaviors …