@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{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{WeidlRaiynBerres2023, author = {Weidl, Galia and Raiyn, Jamal and Berres, Stefan}, title = {Does a livable city profit from a shared CCAM Shuttle Bus on demand?}, series = {International Symposium on Transportation Data \& Modelling (ISTDM2023), June 2023}, booktitle = {International Symposium on Transportation Data \& Modelling (ISTDM2023), June 2023}, year = {2023}, abstract = {Livable cities measure quality-of-life factors such as transportation, convenience of daily life, education, and a safe and stable built and natural environment. Livability of a city includes also some social and psychological factors, like emotion and perception. How do we realize the advantages of new technology under mixed traffic conditions, while observing all daily requirements on safety, convenience and high education needs?}, subject = {Stadtentwicklung}, 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} }