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    <title language="eng">Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events</title>
    <abstract language="eng">This paper investigates the ability of autonomous driving systems to predict outcomes by&#13;
considering human factors like gender, age, and driving experience, particularly in the context of&#13;
safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to&#13;
make plausible deductions, handle conflicting data, and adjust their responses in real-time during&#13;
safety-critical situations. A foundational dataset, which encompasses various driving scenarios&#13;
such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles&#13;
to exhibit appropriate behavior and make sound decisions in critical safety events. The deep&#13;
learning model incorporates personalized cognitive agents for each driver, considering their distinct&#13;
preferences, characteristics, and requirements. This personalized approach aims to enhance the&#13;
safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent&#13;
transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall&#13;
performance within autonomous driving systems. To describe the causal relationship between external&#13;
factors like weather conditions and human factors, and safety-critical driver behaviors, various&#13;
data mining techniques can be applied. One commonly used method is regression analysis. Additionally,&#13;
correlation analysis is employed to reveal relationships between different factors, helping to&#13;
identify the strength and direction of their impact on safety-critical driver behavior.&#13;
Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical&#13;
events; cognitive vehicles&#13;
1. Introduction&#13;
Despite the increasing prevalence of vehicle automation, the persistently high number&#13;
of car crashes remains a concern. Safety-critical events in human-driven scenarios have&#13;
become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating&#13;
human driving behavior is imperative to establish traffic baselines for mixed&#13;
traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various&#13;
factors, such as weather conditions affecting visibility in longitudinal car-following (CF)&#13;
behavior [1,2], influence human driving behavior [3].&#13;
Car-following behavior, illustrating how a following vehicle responds to the lead&#13;
vehicle in the same lane, is a crucial aspect. Existing car-following models often make&#13;
assumptions about homogeneous drivers, neglecting significant heterogeneity in driving&#13;
experience, gender, character, emotions, and sociological, psychological, and physiological&#13;
traits. Failing to account for this heterogeneity hampers a comprehensive understanding of&#13;
car-following behavior, limiting model accuracy and applicability. In the development of&#13;
more realistic car-following models for mixed traffic, acknowledging the diversity among&#13;
drivers is crucial. By including individual variations such as risk-taking tendencies, reaction&#13;
times, decision-making processes, and driving styles, the modeling of real-world&#13;
driving complexities can be improved. Simplifying drivers into a few categories overlooks&#13;
the richness and variety of their characteristics, prompting the need for a more comprehensive&#13;
approach to capture nuances within different driver profiles. To address these&#13;
Smart Cities</abstract>
    <parentTitle language="eng">Smart Cities</parentTitle>
    <identifier type="url">https://www.mdpi.com/2624-6511/7/1/18</identifier>
    <identifier type="doi">https://doi.org/10.3390/ smartcities7010018</identifier>
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    <author>Jamal Raiyn</author>
    <author>Galia Weidl</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autonomes Fahrzeug</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mensch-Maschine-Kommunikation</value>
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    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Business Transformation and Innovation Management</collection>
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    <completedDate>2024-10-17</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis of Driving Behavior in Adverse Weather Conditions</title>
    <abstract language="eng">This paper discusses the impact of Connected Cooperative and Automated Mobility (CCAM) on safety-critical events. The replacement of human drivers by autonomous vehicles (AVs) is promising improved traffic efficiency and reduction of car- crashes to zero using a baseline network traffic. Predicting driving behavior during car-following has been crucial for enhancing road safety while developing advanced driver assistance systems with adaptive cruise control. Human factors significantly influence the driving behavior of a vehicle. Thus, understanding the causal relations between human factors and driving behavior is essential for accurate prediction of vehicle behavior. This is important when autonomous vehicles are expected to behave (cooperatively, according to traffic rules and good praxis) in a human predictable manner, while driving in mixed traffic, involving autonomous, automated, and human driven vehicles. In this paper, we propose a methodology that combines convolutional neural networks (CNNs) with human factors analysis to predict driving behavior during car-following under adverse weather conditions (AWCs).</abstract>
    <parentTitle language="deu">IEEE 7th International Conference AND workshop in Óbuda on Electrical and Power Engineering (CANDO EPE 2024)</parentTitle>
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    <author>Jamal Raiyn</author>
    <author>Mohamad Mofeed Chaar</author>
    <author>Galia Weidl</author>
    <subject>
      <language>deu</language>
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      <value>Autonomes Fahrzeug</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
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      <value>Wetter</value>
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    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
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    <title language="eng">Improving the Perception of Objects under Foggy Conditions in the Surrounding Environment</title>
    <abstract language="eng">Autonomous Driving (AD) technology has rapidly advanced in recent years. Some challenges remain, particularly in ensuring robust performance under adverse weather conditions, like heavy fog. To address this, we propose a multi-class fog density classification approach to enhance the performance of AD systems. By dividing the fog density into multiple classes (25\%, 50\%, 75\%, and 100\%) and generating separate data-sets for each class using the Carla simulator, we can independently improve perception for each fog density and examine the effects of fog at each level. This approach offers several advantages, including improved perception, targeted training, and enhanced generalizability. The results show improved perception of objects from the categories: cars, buses, trucks, vans, pedestrians, and traffic lights. Our multi-class fog density approach is a promising step towards achieving robust AD system performance under adverse weather conditions.</abstract>
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    <title language="eng">Assessing Inattentiveness and Human Elements in Critical Driving Safety Events</title>
    <abstract language="eng">Road accidents, with their potential for severe consequences, pose an ongoing global challenge. Within the multitude of factors contributing to these incidents, inattentiveness and the intricate human elements inherent in driving behaviors stand out as pivotal. As indicated by reports and studies on traffic safety, a significant share of accidents can be attributed to driver inattentiveness, encompassing activities such as texting, talking on the phone, or simply being distracted by the surrounding environment. Beyond these observable behaviors lie complex human elements, influenced by factors ranging from cognitive processes to emotional states, which significantly contribute to the occurrence and severity of critical safety events. Inattentiveness is defined as a state in which a driver's eye gaze behavior deviates from attentive driving patterns. It can be influenced by human factors and adverse weather conditions, serving as an indicator of an increased risk of inattentiveness and the potential to contribute to safety-critical events on the road. Recognition of inattentiveness occurs when the average gaze duration on the road or critical areas falls below a specified threshold. The driver's response time is crucial to the braking process of the vehicle and, therefore, has a significant impact on safety in critical situations.</abstract>
    <parentTitle language="deu">9th European Congress on Computational Methods in Applied Sciences and Engineering 3-7 June 2024, Lisboa, Portugal</parentTitle>
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    <title language="eng">Predicting Depth Maps from Single RGB Images and Addressing Missing Information in Depth Estimation</title>
    <abstract language="eng">Depth imaging is a crucial area in Autonomous Driving Systems (ADS), as it plays a key role in detecting and measuring objects in the vehicle’s surroundings. However, a significant challenge in this domain arises from missing information in Depth images, where certain points are not measurable due to gaps or inconsistencies in pixel data. Our research addresses two key tasks to overcome this challenge. First, we developed an algorithm using a multi-layered training approach to generate Depth images from a single RGB image. Second, we addressed the issue of missing information in Depth images by applying our algorithm to rectify these gaps, resulting in Depth images with complete and accurate data. We further tested our algorithm on the Cityscapes dataset and successfully resolved the missing information in its Depth images, demonstrating the effectiveness of our approach in real-world urban environments.</abstract>
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