[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82560-en":3,"doc-seo-82560-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82560,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Investigating Driver Behavior in Complex Traffic Situations While Driving Partially Automated Vehicles","Traffic complexity critically shapes driver workload in partially automated vehicles, yet the relationship between subjective traffic perception and measurable behavioral indicators remains insufficiently studied in real-world conditions. This paper examines driver–vehicle interaction, glance patterns, and guiding fixation across varying perceived complexity levels using real data from 20 drivers in urban traffic. Complexity is established via expert labeling as ground truth. Statistical analysis of 16 metrics shows defensive adaptation, including increased speed-limit deviation, higher brake rate with reduced braking intensity, and entropy-driven changes in gaze and guiding fixation.","Investigating Driver Behavior in Complex Traffic Situations While  \nDriving Partially Automated Vehicles  \nLukas Kning 1 ,2 , Nataˇsa Milii2 , Klaus Bogenberger1  \narXiv :2607 .00855v1 [ ee ss . SY] 1 Jul 2026  \nAbstract—Traffic complexity critically influences driver task demands in partially automated vehicles, yet subjective perception and its behavioral indicators remain underexplored in real-world settings. This paper analyzes driver behavior vehicle interaction, glance patterns, and guiding fixation-across varying levels of subjective traffic complexity, using real-world data from 20 drivers in real urban traffic. Traffic complexity was determined by expert labeling and served as ground truth for vehicle data. Statistical analysis of 16 driver behavior metrics revealed small but significant trends with increasing complexity: deviation from speed limit increased, brake rate increased while braking intensity decreased, horizontal gaze dispersion and entropy widened, and guiding fixation rate decreased, indicating defensive adaptation and perceptual shifts. Contributions include real-world validation of gaze metrics and guiding fixation under subjective complexity, novel insights from gaze and guiding fixation entropy metrics, and the identification of promising indicators (driven speed, brake rate, gaze yaw entropy, guiding fixation rate) for complexity-adaptive partially automated vehicles. While based on a limited urban sample and expert-labeled subjective complexity, the findings provide a foundation for combined complexity scores and their integration into complexity-adaptive, partially automated vehicles, boosting human-like automation and enhancing safety and predictability in the traffic system.  \nI. INTRODUCTION  \nDriving on a winding road could be a real pleasure in perfect weather with no traffic, or a demanding, complex task in foggy weather during rush hour. These differences in perceived subjective traffic complexity directly affect driver behavior, the demands placed on Advanced Driver Assistance System (ADAS), and traffic as a whole.  \nModern partially automated ADAS (SAE Level 2) takeover lateral and longitudinal control, but the driver must still supervise the system [1] . Maintaining driver engagement is challenging, as current systems often rely on punitive inattention warnings. Developing ADAS that promote active participation is thus a promising approach to enhancing road safety. Understanding the current demand placed on the driver by the driving task is crucial to providing optimal driving assistance that promotes active participation and avoids overload.  \nOne important factor is the complexity of the current traffic situation, which strongly influences task demand, particularly  \nThis work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by BMW’s ethics committee, and performed in line with the Declaration of Helsinki.  \n1Chair of Traffic Engineering and Control, Technical University of Munich, 80333 Munich, Germany [lukas.koening@tum.de](lukas.koening@tum.de) , [klaus.bogenberger@tum.de](klaus.bogenberger@tum.de)  \n2 BMW AG, 80809 Munich, [Germany](Germany natasa.milicic@bmw.de)[ natasa.milicic@bmw.de](Germany natasa.milicic@bmw.de)  \nfor perception tasks [2] . Drivers tend to drive more defensively, reducing speed and increasing braking as complexity increases [3], [4] . Furthermore, increased complexity reduces lane-keeping performance and raises the probability of errors [2] . In the context of automated driving, research found declining take-over performance as complexity increases [5] .  \nWhile many approaches quantify objective traffic complexity from environmental features, only a few studies examine driver behavior across levels of perceived subjective traffic complexity [6] . Research has identified wider gaze behavior in more complex scenarios [7] . More concretely, a driving-simulator study reported increas","cbCaiigRbSK5fJLf","https://ap.wps.com/l/cbCaiigRbSK5fJLf","pdf",2542852,5,1,7,"English","en",105,"# Introduction\n## Driver workload and ADAS engagement\n## Traffic complexity and behavioral effects\n## Gaze behavior and information-theoretic measures\n## Guiding fixation and research gap\n## Research question and contributions","[{\"question\":\"What is the main research focus of the paper?\",\"answer\":\"The paper studies how driver behavior—vehicle interaction, glance behavior, and guiding fixation—changes across traffic situations with different levels of subjective traffic complexity in partially automated driving.\"},{\"question\":\"How is traffic complexity defined in the study?\",\"answer\":\"Traffic complexity is determined by expert labeling and used as ground truth for the corresponding vehicle data.\"},{\"question\":\"What behavioral trends were found as subjective complexity increases?\",\"answer\":\"With increasing complexity, drivers show defensive adaptation: greater deviation from the speed limit, an increased brake rate alongside decreased braking intensity, wider and more uncertain gaze behavior, and a reduced guiding fixation 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is the main research focus of the paper?","Question",{"text":76,"@type":77},"The paper studies how driver behavior—vehicle interaction, glance behavior, and guiding fixation—changes across traffic situations with different levels of subjective traffic complexity in partially automated driving.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is traffic complexity defined in the study?",{"text":81,"@type":77},"Traffic complexity is determined by expert labeling and used as ground truth for the corresponding vehicle data.",{"name":83,"@type":74,"acceptedAnswer":84},"What behavioral trends were found as subjective complexity increases?",{"text":85,"@type":77},"With increasing complexity, drivers show defensive adaptation: greater deviation from the speed limit, an increased brake rate alongside decreased braking intensity, wider and more uncertain gaze behavior, and a reduced guiding fixation 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