@inproceedings{RaiynWeidl2023, author = {Raiyn, Jamal and Weidl, Galia}, title = {Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments}, series = {IEEE International Smart Cities Conference (ISC2)}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, doi = {10.1109/ISC257844.2023.10293463}, year = {2023}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{ChaarWeidlRaiyn2023, author = {Chaar, Mohamad Mofeed and Weidl, Galia and Raiyn, Jamal}, title = {Analyse the effect of fog on the perception}, series = {Conference: International Symposium on Transportation Data \& Modelling (ISTDM 2023)}, booktitle = {Conference: International Symposium on Transportation Data \& Modelling (ISTDM 2023)}, year = {2023}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{RaiynWeidl2023, author = {Raiyn, Jamal and Weidl, Galia}, title = {Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network}, series = {VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems}, booktitle = {VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems}, doi = {10.5220/0011839600003479}, year = {2023}, abstract = {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.}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{JaroschNaujoksWandtneretal.2019, author = {Jarosch, Oliver and Naujoks, Frederik and Wandtner, Bernhard and Gold, Christian and Marberger, Claus and Weidl, Galia and Schrauf, Michael}, title = {The Impact of Non-Driving Related Tasks on Take-over Performance in Conditionally Automated Driving - A Review of the Empirical Evidence}, series = {9. Tagung Automatisiertes Fahren, M{\"u}nchen, Partner T{\"U}V S{\"u}d, November 2019}, booktitle = {9. Tagung Automatisiertes Fahren, M{\"u}nchen, Partner T{\"U}V S{\"u}d, November 2019}, year = {2019}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{RaiynWeidl2024, author = {Raiyn, Jamal and Weidl, Galia}, title = {Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events}, series = {Smart Cities}, volume = {7}, journal = {Smart Cities}, number = {1}, doi = {https://doi.org/10.3390/ smartcities7010018}, pages = {460 -- 474}, year = {2024}, abstract = {This paper investigates the ability of autonomous driving systems to predict outcomes by considering human factors like gender, age, and driving experience, particularly in the context of safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to make plausible deductions, handle conflicting data, and adjust their responses in real-time during safety-critical situations. A foundational dataset, which encompasses various driving scenarios such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles to exhibit appropriate behavior and make sound decisions in critical safety events. The deep learning model incorporates personalized cognitive agents for each driver, considering their distinct preferences, characteristics, and requirements. This personalized approach aims to enhance the safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall performance within autonomous driving systems. To describe the causal relationship between external factors like weather conditions and human factors, and safety-critical driver behaviors, various data mining techniques can be applied. One commonly used method is regression analysis. Additionally, correlation analysis is employed to reveal relationships between different factors, helping to identify the strength and direction of their impact on safety-critical driver behavior. Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical events; cognitive vehicles 1. Introduction Despite the increasing prevalence of vehicle automation, the persistently high number of car crashes remains a concern. Safety-critical events in human-driven scenarios have become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating human driving behavior is imperative to establish traffic baselines for mixed traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various factors, such as weather conditions affecting visibility in longitudinal car-following (CF) behavior [1,2], influence human driving behavior [3]. Car-following behavior, illustrating how a following vehicle responds to the lead vehicle in the same lane, is a crucial aspect. Existing car-following models often make assumptions about homogeneous drivers, neglecting significant heterogeneity in driving experience, gender, character, emotions, and sociological, psychological, and physiological traits. Failing to account for this heterogeneity hampers a comprehensive understanding of car-following behavior, limiting model accuracy and applicability. In the development of more realistic car-following models for mixed traffic, acknowledging the diversity among drivers is crucial. By including individual variations such as risk-taking tendencies, reaction times, decision-making processes, and driving styles, the modeling of real-world driving complexities can be improved. Simplifying drivers into a few categories overlooks the richness and variety of their characteristics, prompting the need for a more comprehensive approach to capture nuances within different driver profiles. To address these Smart Cities}, subject = {Autonomes Fahrzeug}, language = {en} } @incollection{MadsenWeidl2024, author = {Madsen, Anders L. and Weidl, Galia}, title = {Bayes'sche Netze als Methode zur Implementierung transparenter, erkl{\"a}rbarer und vertrauensw{\"u}rdiger K{\"u}nstlicher Intelligenz}, series = {Vertrauen in K{\"u}nstliche Intelligenz - Eine multi-perspektivische Betrachtung}, booktitle = {Vertrauen in K{\"u}nstliche Intelligenz - Eine multi-perspektivische Betrachtung}, publisher = {Springer Verlag}, pages = {139 -- 162}, year = {2024}, abstract = {Dieser Beitrag betrachtet die Verwendung von Bayes'schen Netzen als Methode zur Implementierung von transparenter, erkl{\"a}rbarer und vertrauensw{\"u}rdiger K{\"u}nstlicher Intelligenz (KI). Er beginnt mit einer Darstellung und Diskussion von Schl{\"u}sselkonzepten im Zusammenhang mit der Verwendung von Methoden der K{\"u}nstlichen Intelligenz und der Implementierung von erkl{\"a}rbarer und vertrauensw{\"u}rdiger KI. Der Beitrag diskutiert mehrere Konzepte aus dem Bereich der Bayes'schen Netze, die f{\"u}r die praktische Anwendung dieser Modelle in Systemen mit KI relevant sind. Zu den betrachteten Konzepten geh{\"o}ren unter anderem der Bayes-Faktor, die wahrscheinlichste Erkl{\"a}rung und die relevanteste Erkl{\"a}rung. Der Beitrag endet mit einem Fallbeispiel aus dem Bereich des automatisierten Fahrens, das veranschaulicht, wie transparente und erkl{\"a}rbare KI mithilfe von Bayes'schen Netzen umgesetzt werden kann, um eine vertrauensw{\"u}rdige L{\"o}sung zu schaffen.}, subject = {K{\"u}nstliche Intelligenz}, language = {de} } @inproceedings{RaiynChaarWeidl2024, author = {Raiyn, Jamal and Chaar, Mohamad Mofeed and Weidl, Galia}, title = {Analysis of Driving Behavior in Adverse Weather Conditions}, series = {IEEE 7th International Conference AND workshop in {\´O}buda on Electrical and Power Engineering (CANDO EPE 2024)}, booktitle = {IEEE 7th International Conference AND workshop in {\´O}buda on Electrical and Power Engineering (CANDO EPE 2024)}, year = {2024}, abstract = {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).}, subject = {Autonomes Fahrzeug}, language = {en} } @unpublished{ChaarRaiynWeidl2024, author = {Chaar, Mohamad Mofeed and Raiyn, Jamal and Weidl, Galia}, title = {Improving the Perception of Objects under Foggy Conditions in the Surrounding Environment}, publisher = {Research Square Platform LLC}, doi = {https://doi.org/10.21203/rs.3.rs-3821656/v1}, year = {2024}, abstract = {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.}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{ChaarRaiynWeidl2025, author = {Chaar, Mohamad Mofeed and Raiyn, Jamal and Weidl, Galia}, title = {Predicting Depth Maps from Single RGB Images and Addressing Missing Information in Depth Estimation}, series = {Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems}, booktitle = {Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems}, publisher = {SCITEPRESS - Science and Technology Publications}, doi = {10.5220/0013365900003941}, pages = {549 -- 556}, year = {2025}, abstract = {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.}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{TalluriMadsenWeidl2025, author = {Talluri, Kranthi Kumar and Madsen, Anders L. and Weidl, Galia}, title = {Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks}, series = {2025 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, doi = {10.48550/arXiv.2505.02050}, year = {2025}, abstract = {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.}, subject = {Unfallverh{\"u}tung}, language = {en} }