{"id":{"repo_id":"tu-berlin","oai_identifier":"oai:depositonce.tu-berlin.de:11303/21859"},"canonical_url":"https://search.dev.ndltd.org/etd/tu-berlin/oai:depositonce.tu-berlin.de:11303/21859","repository":{"repo_id":"tu-berlin","name":"Technische Universität Berlin","base_url":"https://api-depositonce.tu-berlin.de/server/oai/request"},"display":{"title":"Smart agents","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Rock, Teresa"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Marker, Stefanie"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-27T21:28:49Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.14279/depositonce-20660"],"render_values":[{"text":"https://doi.org/10.14279/depositonce-20660","href":"https://doi.org/10.14279/depositonce-20660","code":true}]}]},"links":{"outbound_url":"https://depositonce.tu-berlin.de/handle/11303/21859","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Marker, Stefanie"]},{"key":"dc:creator","label":"Author","values":["Rock, Teresa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-09-24T12:46:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-09-24T12:46:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://depositonce.tu-berlin.de/handle/11303/21859","https://doi.org/10.14279/depositonce-20660"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.","Der Bedarf an intelligenten Fahrermodellen, die in der Lage sind, komplexe Verkehrssituationen in einer Weise zu bewältigen, die dem menschlichen Verhalten ähnelt, ergibt sich aus verschiedenen Anwendungsbereichen. Im Kontext des autonomen Fahrens werden Fahrermodelle verwendet, um den menschlichen Fahrer zu ersetzen und sichere sowie flexible Lösungen für die Mobilität anzubieten. Man geht davon aus, dass autonome Fahrzeuge, die sich menschlich verhalten, für sicherere Verkehrsinteraktionen sorgen und außerdem eher von Nutzern akzeptiert werden [1, 2]. Auch im Bereich der Fahrsimulation werden Fahrermodelle benötigt, um den Umgebungsverkehr in der Simulation zu erzeugen, der möglichst dem Verkehrsverhalten in der realen Welt entsprechen soll. Fahrsimulation hat sich zu einem zentralen und unverzichtbaren Werkzeug für Forschung und Entwicklung im Verkehrssektor entwickelt. Darüber hinaus führen globale Trends wie Globalisierung, Nachhaltigkeit und die steigende Nachfrage an Mobilität zu einem erhöhten Forschungsbedarf, insbesondere im urbanen Kontext [3, 4]. Somit kann das Verständnis und die Modellierung des menschlichen Fahrverhaltens in urbanen Umgebungen als ein Forschungsgebiet mit wachsender Bedeutung für die zukünftige Mobilität angesehen werden. Dieser zunehmenden Bedeutung steht jedoch ein derzeit unvollständiger Forschungsstand gegenüber, da sich die meisten Arbeiten entweder auf einfache Autobahnszenarien konzentrieren oder Ansätze zur Lösung isolierter Szenarien präsentieren. Dementsprechend sind aktuelle Lösungsansätze nicht für die für die Vielfalt und Komplexität der im Stadtverkehr auftretenden Situationen geeignet. Ziel dieser Arbeit ist es daher, übertragbare und praktikable Methoden für die Modellierung von menschenähnlichem Fahrverhalten in urbanen Umgebungen zu erarbeiten. Um diese wissenschaftliche Lücke zu adressieren, wird ein zweistufiger Ansatz verfolgt, indem zunächst eine detaillierte Analyse des interdisziplinären Themas durchgeführt wird, um Kernprobleme moderner Lösungen zu identifizieren, welche anschließend im zweiten Teil mit neuartigen Methoden behandelt werden. Entsprechend der interdisziplinären Natur des Themas werden verschiedenste Forschungsbereiche wie Psychologie, Robotik, Fahrsimulation oder autonomes Fahren beleuchtet. Ausgehend davon werden klare Modellanforderungen identifiziert und die folgenden vier Kernherausforderungen abgeleitet, welche im Methodenteil mit innovativen Methoden adressiert und kritisch diskutiert werden: Repräsentation von Umgebungsinformationen zur Ausbildung eines situativen Verständnisses in Modellen, Entwicklung und Untersuchung generalisierbarer Vorhersagemodellen, dynamische Entscheidungsfindung für situationsadaptives Verhalten und aussagekräftige Evaluationsstrategien zur Bewertung der Menschenähnlichkeit von Modellverhalten. Eine umfassende Diskussion der Ergebnisse, Limitationen und ein Ausblick auf weiterführende Forschungsthemen runden die Arbeit ab."]},{"key":"dc:title","label":"Title","values":["Smart agents"]}]}],"canonical_facts":{"dc:contributor.advisor":["Marker, Stefanie"],"dc:creator":["Rock, Teresa"],"dc:date.accessioned":["2024-09-24T12:46:22Z"],"dc:date.available":["2024-09-24T12:46:22Z"],"dc:date.issued":["2024"],"dc:description.abstract":["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.","Der Bedarf an intelligenten Fahrermodellen, die in der Lage sind, komplexe Verkehrssituationen in einer Weise zu bewältigen, die dem menschlichen Verhalten ähnelt, ergibt sich aus verschiedenen Anwendungsbereichen. Im Kontext des autonomen Fahrens werden Fahrermodelle verwendet, um den menschlichen Fahrer zu ersetzen und sichere sowie flexible Lösungen für die Mobilität anzubieten. Man geht davon aus, dass autonome Fahrzeuge, die sich menschlich verhalten, für sicherere Verkehrsinteraktionen sorgen und außerdem eher von Nutzern akzeptiert werden [1, 2]. Auch im Bereich der Fahrsimulation werden Fahrermodelle benötigt, um den Umgebungsverkehr in der Simulation zu erzeugen, der möglichst dem Verkehrsverhalten in der realen Welt entsprechen soll. Fahrsimulation hat sich zu einem zentralen und unverzichtbaren Werkzeug für Forschung und Entwicklung im Verkehrssektor entwickelt. Darüber hinaus führen globale Trends wie Globalisierung, Nachhaltigkeit und die steigende Nachfrage an Mobilität zu einem erhöhten Forschungsbedarf, insbesondere im urbanen Kontext [3, 4]. Somit kann das Verständnis und die Modellierung des menschlichen Fahrverhaltens in urbanen Umgebungen als ein Forschungsgebiet mit wachsender Bedeutung für die zukünftige Mobilität angesehen werden. Dieser zunehmenden Bedeutung steht jedoch ein derzeit unvollständiger Forschungsstand gegenüber, da sich die meisten Arbeiten entweder auf einfache Autobahnszenarien konzentrieren oder Ansätze zur Lösung isolierter Szenarien präsentieren. Dementsprechend sind aktuelle Lösungsansätze nicht für die für die Vielfalt und Komplexität der im Stadtverkehr auftretenden Situationen geeignet. Ziel dieser Arbeit ist es daher, übertragbare und praktikable Methoden für die Modellierung von menschenähnlichem Fahrverhalten in urbanen Umgebungen zu erarbeiten. Um diese wissenschaftliche Lücke zu adressieren, wird ein zweistufiger Ansatz verfolgt, indem zunächst eine detaillierte Analyse des interdisziplinären Themas durchgeführt wird, um Kernprobleme moderner Lösungen zu identifizieren, welche anschließend im zweiten Teil mit neuartigen Methoden behandelt werden. Entsprechend der interdisziplinären Natur des Themas werden verschiedenste Forschungsbereiche wie Psychologie, Robotik, Fahrsimulation oder autonomes Fahren beleuchtet. Ausgehend davon werden klare Modellanforderungen identifiziert und die folgenden vier Kernherausforderungen abgeleitet, welche im Methodenteil mit innovativen Methoden adressiert und kritisch diskutiert werden: Repräsentation von Umgebungsinformationen zur Ausbildung eines situativen Verständnisses in Modellen, Entwicklung und Untersuchung generalisierbarer Vorhersagemodellen, dynamische Entscheidungsfindung für situationsadaptives Verhalten und aussagekräftige Evaluationsstrategien zur Bewertung der Menschenähnlichkeit von Modellverhalten. Eine umfassende Diskussion der Ergebnisse, Limitationen und ein Ausblick auf weiterführende Forschungsthemen runden die Arbeit ab."],"dc:identifier.uri":["https://depositonce.tu-berlin.de/handle/11303/21859","https://doi.org/10.14279/depositonce-20660"],"dc:language.iso":["en"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:title":["Smart agents"],"dc:type":["Doctoral Thesis"]},"updated_at":"2026-07-27T21:28:49Z"}