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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>
    <parentTitle language="eng">Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems</parentTitle>
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    <author>Jamal Raiyn</author>
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      <value>Autonomes Fahrzeug</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Tiefenbild</value>
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    <title language="eng">Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks</title>
    <abstract language="eng">Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems.</abstract>
    <parentTitle language="deu">2025 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
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    <author>Kranthi Kumar Talluri</author>
    <author>Anders L. Madsen</author>
    <author>Galia Weidl</author>
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      <language>deu</language>
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      <value>Unfallverhütung</value>
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      <language>deu</language>
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      <value>Schnellfahrstrecke</value>
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      <language>deu</language>
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      <value>Autonomes Fahrzeug</value>
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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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    <author>Mohamad Mofeed Chaar</author>
    <author>Jamal Raiyn</author>
    <author>Galia Weidl</author>
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      <value>Autonomes Fahrzeug</value>
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    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Wetter</value>
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    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Sicht</value>
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    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
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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>
    <identifier type="url">https://www.researchgate.net/publication/385591980_ANALYSIS_OF_DRIVING_BEHAVIOR_IN_ADVERSE_WEATHER_CONDITIONS</identifier>
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    <author>Jamal Raiyn</author>
    <author>Mohamad Mofeed Chaar</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>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Wetter</value>
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    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
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    <language>deu</language>
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    <publisherName>Springer Verlag</publisherName>
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    <completedDate>2024-09-19</completedDate>
    <publishedDate>2024-09-19</publishedDate>
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    <title language="deu">Bayes’sche Netze als Methode zur Implementierung transparenter, erklärbarer und vertrauenswürdiger Künstlicher Intelligenz</title>
    <abstract language="deu">Dieser Beitrag betrachtet die Verwendung von Bayes’schen Netzen als Methode zur Implementierung von transparenter, erklärbarer und vertrauenswürdiger Künstlicher Intelligenz (KI). Er beginnt mit einer Darstellung und Diskussion von Schlüsselkonzepten im Zusammenhang mit der Verwendung von Methoden der Künstlichen Intelligenz und der Implementierung von erklärbarer und vertrauenswürdiger KI. Der Beitrag diskutiert mehrere Konzepte aus dem Bereich der Bayes’schen Netze, die für die praktische Anwendung dieser Modelle in Systemen mit KI relevant sind. Zu den betrachteten Konzepten gehören unter anderem der Bayes-Faktor, die wahrscheinlichste Erklärung und die relevanteste Erklärung. Der Beitrag endet mit einem Fallbeispiel aus dem Bereich des automatisierten Fahrens, das veranschaulicht, wie transparente und erklärbare KI mithilfe von Bayes’schen Netzen umgesetzt werden kann, um eine vertrauenswürdige Lösung zu schaffen.</abstract>
    <parentTitle language="deu">Vertrauen in Künstliche Intelligenz - Eine multi-perspektivische Betrachtung</parentTitle>
    <identifier type="url">https://link.springer.com/chapter/10.1007/978-3-658-43816-6_8</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Anders L. Madsen</author>
    <author>Galia Weidl</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Bayes'sche Netze</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Automatisiertes Fahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Mischverkehr</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bayes-Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autonomes Fahrzeug</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
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    <completedYear>2024</completedYear>
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    <language>eng</language>
    <pageFirst>460</pageFirst>
    <pageLast>474</pageLast>
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    <issue>1</issue>
    <volume>7</volume>
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    <completedDate>2024-02-01</completedDate>
    <publishedDate>2024-02-01</publishedDate>
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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>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Business Transformation and Innovation Management</collection>
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    <id>2162</id>
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    <title language="eng">The Impact of Non-Driving Related Tasks on Take-over Performance in Conditionally Automated Driving – A Review of the Empirical Evidence</title>
    <abstract language="eng">Conditional automated driving (CAD) systems (SAE level 3) will soon be introduced to the public market. This automation level is designed to take care of all aspects of the dynamic driving task in specific application areas and does not require the driver to continuously monitor the system performance. However, in contrast to higher levels of automation the "fallback-ready" user always has to be able to regain control if requested by the system. As CAD allows the driver to engage in non-driving-related tasks (NDRTs) past human factors research has looked at their effects on takeover time and quality especially in short-term takeover situations. In order to understand how takeover performance is impacted by different NDRTs, this paper summarizes and compares available results according to the NDRT's impact on the sensoric, motoric and cognitive transition. In addition, aspects of arousal and motivation are considered. Due to the heterogeneity of the empirical work and the available data practically relevant effects can only be attested for NDRTs that cause severe discrepancies between the current driver state and the requirements of the takeover task, such as sensoric and motoric unavailability. The paper concludes by discussing methodological issues and recommending the development of standardized methods for the future.</abstract>
    <parentTitle language="deu">9. Tagung Automatisiertes Fahren, München, Partner TÜV Süd, November 2019</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/338644809_The_Impact_of_Non-Driving_Related_Tasks_on_Take-over_Performance_in_Conditionally_Automated_Driving_-_A_Review_of_the_Empirical_Evidence#fullTextFileContent</identifier>
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    <title language="eng">Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network</title>
    <abstract language="eng">The new generation of autonomous vehicles (AVs) are being designed to act autonomously and collect travel data based on various smart devices and sensors. The goal is to enable AVs to operate under their own power. Naturalistic driving studies (NDSs) collect data continuously from real traffic activities, in order not to miss any safety-critical event. In NDSs of AVs, however, the data they collect is influenced by various sources that degrade their forecasting accuracy. A convolutional neural network (CNN) is proposed to process a large amount of traffic data in different formats. A CNN can detect anomalies in traffic data that negatively affect traffic efficiency and identify the source of data anomalies, which can help reduce traffic congestion and vehicular queuing.</abstract>
    <parentTitle language="eng">VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems</parentTitle>
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    <author>Jamal Raiyn</author>
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    <title language="eng">Analyse the effect of fog on the perception</title>
    <parentTitle language="eng">Conference: International Symposium on Transportation Data &amp; Modelling (ISTDM 2023)</parentTitle>
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    <author>Mohamad Mofeed Chaar</author>
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    <title language="eng">Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments</title>
    <parentTitle language="eng">IEEE International Smart Cities Conference (ISC2)</parentTitle>
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    <author>Jamal Raiyn</author>
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