{"id":{"repo_id":"tu-berlin","oai_identifier":"oai:depositonce.tu-berlin.de:11303/25940"},"canonical_url":"https://search.dev.ndltd.org/etd/tu-berlin/oai:depositonce.tu-berlin.de:11303/25940","repository":{"repo_id":"tu-berlin","name":"Technische Universität Berlin","base_url":"https://api-depositonce.tu-berlin.de/server/oai/request"},"display":{"title":"Towards situation-aware driving style adaptation","abstract":"With advancing technological capabilities, the focus of future driver assistance systems and autonomous vehicles is shifting from mere feasibility to implementing acceptable and comfortable driving characteristics for future driver assistance systems and autonomous vehicles. This dissertation explores the heterogeneity of human driving behavior, particularly emphasizing lateral driving behavior and individual driving style preferences on rural roads, fostering research toward more personalized driver assistance systems and autonomous vehicles. A controlled, real-world vehicle study (N = 62) was conducted to identify and define key indicators of driving behavior, analyze sociodemographic dependencies and assess the alignment between subjective self-assessments and objective data. Correlation analyses revealed modest but significant associations between participants’ self-reported driving styles and objective acceleration and jerk statistics. Notably, an in-depth analysis of lateral driving behavior highlighted substantial inter-driver variability. Despite growing interest in adaptive driving styles, comprehensive research on the influence of contextual factors, especially adverse weather conditions, and oncoming traffic, on automated driving functions remains limited. To address this gap, a driving simulator study (N = 42) was carried out to examine lateral driving style preferences for autonomous vehicles under varying weather and traffic conditions. Statistical analyses demonstrated a prevalent preference for passive driving styles, with environmental factors significantly affecting perceived comfort during autonomous rides. To overcome the limitations of existing driving style models in integrating environmental information, this work introduces a situation-aware driving style adaptation method that combines deep learning and statistical approaches. By employing visual feature encoders pretrained on fleet data, the proposed method captures a structured representation of the driving environment and learns a mapping from situational context to driving behavior, enabling adaptation to individual driving style preferences. The developed models significantly outperform baseline approaches and form coherent situation clusters, enhancing the adaptability of automated driving systems. These findings contribute to the development of more intuitive and human-centered automation, facilitating the integration of personalized driving styles into future vehicles while considering real-world environmental influences.","abstract_html":"With advancing technological capabilities, the focus of future driver assistance systems and autonomous vehicles is shifting from mere feasibility to implementing acceptable and comfortable driving characteristics for future driver assistance systems and autonomous vehicles. This dissertation explores the heterogeneity of human driving behavior, particularly emphasizing lateral driving behavior and individual driving style preferences on rural roads, fostering research toward more personalized driver assistance systems and autonomous vehicles. A controlled, real-world vehicle study (N = 62) was conducted to identify and define key indicators of driving behavior, analyze sociodemographic dependencies and assess the alignment between subjective self-assessments and objective data. Correlation analyses revealed modest but significant associations between participants’ self-reported driving styles and objective acceleration and jerk statistics. Notably, an in-depth analysis of lateral driving behavior highlighted substantial inter-driver variability. Despite growing interest in adaptive driving styles, comprehensive research on the influence of contextual factors, especially adverse weather conditions, and oncoming traffic, on automated driving functions remains limited. To address this gap, a driving simulator study (N = 42) was carried out to examine lateral driving style preferences for autonomous vehicles under varying weather and traffic conditions. Statistical analyses demonstrated a prevalent preference for passive driving styles, with environmental factors significantly affecting perceived comfort during autonomous rides. To overcome the limitations of existing driving style models in integrating environmental information, this work introduces a situation-aware driving style adaptation method that combines deep learning and statistical approaches. By employing visual feature encoders pretrained on fleet data, the proposed method captures a structured representation of the driving environment and learns a mapping from situational context to driving behavior, enabling adaptation to individual driving style preferences. The developed models significantly outperform baseline approaches and form coherent situation clusters, enhancing the adaptability of automated driving systems. These findings contribute to the development of more intuitive and human-centered automation, facilitating the integration of personalized driving styles into future vehicles while considering real-world environmental influences.","abstract_has_math":false,"creators":["Walser-Haselberger, Johann"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Müller, Steffen"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T21:28:47Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":["https://creativecommons.org/licenses/by-sa/4.0/"],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.14279/depositonce-24767"],"render_values":[{"text":"https://doi.org/10.14279/depositonce-24767","href":"https://doi.org/10.14279/depositonce-24767","code":true}]}]},"links":{"outbound_url":"https://depositonce.tu-berlin.de/handle/11303/25940","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Müller, Steffen"]},{"key":"dc:creator","label":"Author","values":["Walser-Haselberger, Johann"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-12T11:42:24Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-12T11:42:24Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"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":["https://creativecommons.org/licenses/by-sa/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://depositonce.tu-berlin.de/handle/11303/25940","https://doi.org/10.14279/depositonce-24767"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["With advancing technological capabilities, the focus of future driver assistance systems and autonomous vehicles is shifting from mere feasibility to implementing acceptable and comfortable driving characteristics for future driver assistance systems and autonomous vehicles. This dissertation explores the heterogeneity of human driving behavior, particularly emphasizing lateral driving behavior and individual driving style preferences on rural roads, fostering research toward more personalized driver assistance systems and autonomous vehicles. A controlled, real-world vehicle study (N = 62) was conducted to identify and define key indicators of driving behavior, analyze sociodemographic dependencies and assess the alignment between subjective self-assessments and objective data. Correlation analyses revealed modest but significant associations between participants’ self-reported driving styles and objective acceleration and jerk statistics. Notably, an in-depth analysis of lateral driving behavior highlighted substantial inter-driver variability. Despite growing interest in adaptive driving styles, comprehensive research on the influence of contextual factors, especially adverse weather conditions, and oncoming traffic, on automated driving functions remains limited. To address this gap, a driving simulator study (N = 42) was carried out to examine lateral driving style preferences for autonomous vehicles under varying weather and traffic conditions. Statistical analyses demonstrated a prevalent preference for passive driving styles, with environmental factors significantly affecting perceived comfort during autonomous rides. To overcome the limitations of existing driving style models in integrating environmental information, this work introduces a situation-aware driving style adaptation method that combines deep learning and statistical approaches. By employing visual feature encoders pretrained on fleet data, the proposed method captures a structured representation of the driving environment and learns a mapping from situational context to driving behavior, enabling adaptation to individual driving style preferences. The developed models significantly outperform baseline approaches and form coherent situation clusters, enhancing the adaptability of automated driving systems. These findings contribute to the development of more intuitive and human-centered automation, facilitating the integration of personalized driving styles into future vehicles while considering real-world environmental influences.","Mit den fortschreitenden technologischen Möglichkeiten verlagert sich der Fokus zukünftiger Fahrerassistenzsysteme und autonomer Fahrzeuge von der bloßen Machbarkeit hin zur Implementierung akzeptabler und komfortabler Fahreigenschaften. Diese Dissertation untersucht die Heterogenität des menschlichen Fahrverhaltens, insbesondere das laterale Fahrverhalten und individuelle Fahrstilpräferenzen auf Landstraßen, und fördert damit die Forschung hin zu personalisierten Fahrerassistenzsystemen und autonomen Fahrzeugen. Eine kontrollierte, reale Fahrzeugstudie (N = 62) wurde durchgeführt, um zentrale Indikatoren des Fahrverhaltens zu identifizieren und zu definieren, soziodemografische Abhängigkeiten zu analysieren und die Übereinstimmung zwischen subjektiven Selbsteinschätzungen und objektiven Daten zu bewerten. Korrelationsanalysen ergaben moderate, aber signifikante Zusammenhänge zwischen den selbstberichteten Fahrstilen der Teilnehmenden und objektiven Beschleunigungs- sowie Ruckstatistiken. Besonders hervorzuheben ist, dass eine detaillierte Analyse des lateralen Fahrverhaltens eine erhebliche interindividuelle Variabilität aufzeigte. Trotz des wachsenden Interesses an adaptiven Fahrstilen gibt es bislang nur begrenzte Forschung zur Einflussnahme kontextueller Faktoren - insbesondere widriger Wetterbedingungen und Gegenverkehr - auf automatisierte Fahrfunktionen. Um diese Forschungslücke zu schließen, wurde eine Fahrsimulatorstudie (N = 42) durchgeführt, in der laterale Fahrstilpräferenzen für autonome Fahrzeuge unter variierenden Wetter- und Verkehrsbedingungen untersucht wurden. Statistische Analysen zeigten eine weit verbreitete Präferenz für passive Fahrstile, wobei Umweltfaktoren den wahrgenommenen Fahrkomfort erheblich beeinflussten. Um die Einschränkungen bestehender Fahrstilmodelle in der Integration von Umweltinformationen zu überwinden, stellt diese Arbeit eine situationsbewusste Fahrstilanpassungsmethode vor, die Deep Learning mit statistischen Ansätzen kombiniert. Durch den Einsatz visueller Merkmalsencoder, die auf Flottendaten vortrainiert wurden, erfasst die vorgeschlagene Methode eine strukturierte Repräsentation der Fahrumgebung und lernt eine Abbildung vom situativen Kontext auf das Fahrverhalten, wodurch eine Anpassung an individuelle Fahrstilpräferenzen ermöglicht wird. Die entwickelten Modelle übertreffen die Ansätze aus der Literatur signifikant und bilden kohärente Situationscluster, wodurch die Anpassungsfähigkeit automatisierter Fahrzeugsysteme verbessert wird. Diese Erkenntnisse tragen zur Entwicklung intuitiverer und stärker am Menschen orientierter Automatisierung bei und erleichtern die Integration personalisierter Fahrstile in zukünftige Fahrzeuge, wobei reale Umwelteinflüsse berücksichtigt werden."]},{"key":"dc:title","label":"Title","values":["Towards situation-aware driving style adaptation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Müller, Steffen"],"dc:creator":["Walser-Haselberger, Johann"],"dc:date.accessioned":["2025-12-12T11:42:24Z"],"dc:date.available":["2025-12-12T11:42:24Z"],"dc:date.issued":["2025"],"dc:description.abstract":["With advancing technological capabilities, the focus of future driver assistance systems and autonomous vehicles is shifting from mere feasibility to implementing acceptable and comfortable driving characteristics for future driver assistance systems and autonomous vehicles. This dissertation explores the heterogeneity of human driving behavior, particularly emphasizing lateral driving behavior and individual driving style preferences on rural roads, fostering research toward more personalized driver assistance systems and autonomous vehicles. A controlled, real-world vehicle study (N = 62) was conducted to identify and define key indicators of driving behavior, analyze sociodemographic dependencies and assess the alignment between subjective self-assessments and objective data. Correlation analyses revealed modest but significant associations between participants’ self-reported driving styles and objective acceleration and jerk statistics. Notably, an in-depth analysis of lateral driving behavior highlighted substantial inter-driver variability. Despite growing interest in adaptive driving styles, comprehensive research on the influence of contextual factors, especially adverse weather conditions, and oncoming traffic, on automated driving functions remains limited. To address this gap, a driving simulator study (N = 42) was carried out to examine lateral driving style preferences for autonomous vehicles under varying weather and traffic conditions. Statistical analyses demonstrated a prevalent preference for passive driving styles, with environmental factors significantly affecting perceived comfort during autonomous rides. To overcome the limitations of existing driving style models in integrating environmental information, this work introduces a situation-aware driving style adaptation method that combines deep learning and statistical approaches. By employing visual feature encoders pretrained on fleet data, the proposed method captures a structured representation of the driving environment and learns a mapping from situational context to driving behavior, enabling adaptation to individual driving style preferences. The developed models significantly outperform baseline approaches and form coherent situation clusters, enhancing the adaptability of automated driving systems. These findings contribute to the development of more intuitive and human-centered automation, facilitating the integration of personalized driving styles into future vehicles while considering real-world environmental influences.","Mit den fortschreitenden technologischen Möglichkeiten verlagert sich der Fokus zukünftiger Fahrerassistenzsysteme und autonomer Fahrzeuge von der bloßen Machbarkeit hin zur Implementierung akzeptabler und komfortabler Fahreigenschaften. Diese Dissertation untersucht die Heterogenität des menschlichen Fahrverhaltens, insbesondere das laterale Fahrverhalten und individuelle Fahrstilpräferenzen auf Landstraßen, und fördert damit die Forschung hin zu personalisierten Fahrerassistenzsystemen und autonomen Fahrzeugen. Eine kontrollierte, reale Fahrzeugstudie (N = 62) wurde durchgeführt, um zentrale Indikatoren des Fahrverhaltens zu identifizieren und zu definieren, soziodemografische Abhängigkeiten zu analysieren und die Übereinstimmung zwischen subjektiven Selbsteinschätzungen und objektiven Daten zu bewerten. Korrelationsanalysen ergaben moderate, aber signifikante Zusammenhänge zwischen den selbstberichteten Fahrstilen der Teilnehmenden und objektiven Beschleunigungs- sowie Ruckstatistiken. Besonders hervorzuheben ist, dass eine detaillierte Analyse des lateralen Fahrverhaltens eine erhebliche interindividuelle Variabilität aufzeigte. Trotz des wachsenden Interesses an adaptiven Fahrstilen gibt es bislang nur begrenzte Forschung zur Einflussnahme kontextueller Faktoren - insbesondere widriger Wetterbedingungen und Gegenverkehr - auf automatisierte Fahrfunktionen. Um diese Forschungslücke zu schließen, wurde eine Fahrsimulatorstudie (N = 42) durchgeführt, in der laterale Fahrstilpräferenzen für autonome Fahrzeuge unter variierenden Wetter- und Verkehrsbedingungen untersucht wurden. Statistische Analysen zeigten eine weit verbreitete Präferenz für passive Fahrstile, wobei Umweltfaktoren den wahrgenommenen Fahrkomfort erheblich beeinflussten. Um die Einschränkungen bestehender Fahrstilmodelle in der Integration von Umweltinformationen zu überwinden, stellt diese Arbeit eine situationsbewusste Fahrstilanpassungsmethode vor, die Deep Learning mit statistischen Ansätzen kombiniert. Durch den Einsatz visueller Merkmalsencoder, die auf Flottendaten vortrainiert wurden, erfasst die vorgeschlagene Methode eine strukturierte Repräsentation der Fahrumgebung und lernt eine Abbildung vom situativen Kontext auf das Fahrverhalten, wodurch eine Anpassung an individuelle Fahrstilpräferenzen ermöglicht wird. Die entwickelten Modelle übertreffen die Ansätze aus der Literatur signifikant und bilden kohärente Situationscluster, wodurch die Anpassungsfähigkeit automatisierter Fahrzeugsysteme verbessert wird. Diese Erkenntnisse tragen zur Entwicklung intuitiverer und stärker am Menschen orientierter Automatisierung bei und erleichtern die Integration personalisierter Fahrstile in zukünftige Fahrzeuge, wobei reale Umwelteinflüsse berücksichtigt werden."],"dc:identifier.uri":["https://depositonce.tu-berlin.de/handle/11303/25940","https://doi.org/10.14279/depositonce-24767"],"dc:language.iso":["en"],"dc:rights.uri":["https://creativecommons.org/licenses/by-sa/4.0/"],"dc:title":["Towards situation-aware driving style adaptation"],"dc:type":["Doctoral Thesis"]},"updated_at":"2026-07-27T21:28:47Z"}