Smart agents

The demand for intelligent driver models capable of handling complex trafc situations in a way that resembles human behavior arises from various application areas. In the context of Autonomous Driving, driver models replace human drivers, aiming at providing safe and fexible mobility solutions. It is believed that autonomous vehicles exhibiting human-like behavior have the potential to enhance the safety of trafc interactions and are better accepted by users [1, 2]. In the feld of Driving Simulation, driver models are required to generate surrounding trafc within the Virtual Environment to provide a realistic replication of real-world trafc scenarios. Driving Simulation has become a central and indispensable tool for research and development in the transportation sector. Moreover, global trends such as globalization, sustainability, and increased demand for mobility contribute to a growing need for research, especially in the context of urban trafc scenarios [3, 4]. Therefore, modeling and understanding human driving behavior in urban environments shows increasing necessity for the future of mobility. Meanwhile, current research is incomplete, as most publications either focus on more simple highway trafc or propose approaches to solve isolated scenarios or parts of the driving task. As a result, current solutions are not suitable for the diversity and complexity of urban trafc. Therefore, the objective of this thesis is to develop transferable, practicable, and reliable methods for modeling human-like driving behavior in urban environments. In order to address this scientifc gap, a twofold approach is taken. First, a detailed analysis of the topic in its interdisciplinary nature is conducted in order to identify the fundamental problems of modern solutions, which are subsequently addressed with novel methods in the second part of the thesis. Therefore, the topic is explored from the perspective of various research areas, including psychology, robotics, Driving Simulation, and Autonomous Driving. Based on this multidimensional analysis, key challenges in state-of-the-art solutions and clear requirements for modeling human-like driving behavior are determined. The following four key challenges are identifed to prevent successful modeling of human-like driving behavior in urban trafc: representation of complex trafc situations to enable situational understanding, creation and evaluation of generalizable prediction models to anticipate future scene developments, dynamic decision-making to enable situational behavior adaptation, and meaningful evaluation strategies capable of assessing human-like model behavior. Novel methods are presented to address these four main challenges, and the results are critically discussed. A comprehensive discussion of the results, limitations, and an outlook for further research will conclude the thesis.

Author dc:creator

• Rock, Teresa

Advisor dc:contributor.advisor

• Marker, Stefanie

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Identifier URI

https://doi.org/10.14279/depositonce-20660

OAI identifier oai:identifier

oai:depositonce.tu-berlin.de:11303/21859