This paper introduces a method, based on deep reinforcement learning, for automatically
generating a general purpose decision making function. A Deep Q-Network agent was …

While lane change behavior of human drivers has already been widely investigated,
concepts and algorithms to plan lane changes for automated vehicles become necessary …

In the background of autonomous driving at level 3 to level 4, an automated vehicle should
own smarter driving brain to face complicated transportation situations. In order to construct …

This research proposes a unique platform for lane change assistance for generating data-
driven lane change (LC) decisions in vehicular networks. The goal is to reduce the …

Due to the ability to support a wide range of applications and to involve infrastructure
elements, connected and automated vehicles (CAVs) technology has played an important …

The paper demonstrates the validity of sensors and the model in the algorithm for a lane
change controller. The paper presents the systematic derivation of the chosen model from …

The Foresighted Driver Model (FDM) is a microscopic driver model which is based on the
idea that a driver balances risk with utility. This paper deals with the modeling of advanced …

This paper introduces a method for automatically training a general-purpose driver model,
applied to the case of a truck-trailer combination. A genetic algorithm is used to optimize a …

A main difficulty in autonomous driving is the assurance of maneuver acceptability by other
traffic participants. Thus, knowledge about social interaction needs to be incorporated into …

The presented dissertation addresses the problem of risk evaluation and behavior planning
for future intelligent Advanced Driver Assistance Systems (ADAS). For this purpose, a novel …