Will Advanced Driving Assistance Systems (ADAS) and Highly Automated Driving (HAD)
perform in the expected manner? Will they actually make road traffic safer, or will they …

The application of naturalistic driving data (NDD) has the potential to answer critical
research questions in the area of driving behavior assessment, as well as the impact of …

This paper presents a learning-based model predictive trajectory planning controller for
automated driving in unstructured, dynamic environments with obstacle avoidance. We first …

Risk assessment is important for intelligent vehicles to make safe driving decisions. In some
researches, the risk is modeled as the effect of each environment element on the ego …

The survival analysis of driving trajectories allows for holistic evaluations of car-related risks
caused by collisions or curvy roads. This analysis has advantages over common Time-To-X …

Automated vehicles (AVs) are expected to operate on public roads, together with non-
automated vehicles and other road users such as pedestrians or bicycles. Recent ethical …

Interactions between humans and automated vehicles (AVs) will become increasingly
common in future traffic. Cooperative behavior enables comfortable and efficient resolutions …

The task of driving autonomously is difficult due to the vast number of driving situations a
system may be facing. Especially higher levels of automation in less restricted scopes …

Self-driving cars face complex driving situations with a large amount of agents when moving
in crowded cities. However, some of the agents are actually not influencing the behavior of …

Driver support systems that include human states in the support process is an active
research field. Many recent approaches allow, for example, to sense the driver's drowsiness …