TY - CHAP A1 - Chaar, Mohamad Mofeed A1 - Weidl, Galia A1 - Raiyn, Jamal T1 - Analyse the effect of fog on the perception T2 - Conference: International Symposium on Transportation Data & Modelling (ISTDM 2023) KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Nebel Y1 - 2023 UR - https://www.researchgate.net/publication/369484982_Analyse_the_effect_of_fog_on_the_perception#fullTextFileContent ER - TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network T2 - VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - 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. KW - Autonomes Fahrzeug KW - Künstliche Intelligenz Y1 - 2023 UR - https://www.researchgate.net/publication/368332673_Naturalistic_Driving_Studies_Data_Analysis_Based_on_a_Convolutional_Neural_Network#fullTextFileContent U6 - https://doi.org/10.5220/0011839600003479 ER - TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments T2 - IEEE International Smart Cities Conference (ISC2) KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Künstliche Intelligenz KW - Autonomes Fahrzeug Y1 - 2023 UR - https://www.researchgate.net/publication/375128760_Improving_Autonomous_Vehicle_Reasoning_with_Non-Monotonic_Logic_Advancing_Safety_and_Performance_in_Complex_Environments U6 - https://doi.org/10.1109/ISC257844.2023.10293463 ER - TY - CHAP A1 - Weidl, Galia A1 - Raiyn, Jamal A1 - Berres, Stefan T1 - Does a livable city profit from a shared CCAM Shuttle Bus on demand? T2 - International Symposium on Transportation Data & Modelling (ISTDM2023), June 2023 N2 - 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? KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Stadtentwicklung KW - Stadtplanung KW - Digitalisierung Y1 - 2023 UR - https://www.researchgate.net/publication/370492569_Does_a_livable_city_profit_from_a_shared_CCAM_Shuttle_Bus_on_demand#fullTextFileContent ER - TY - JOUR A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events JF - Smart Cities N2 - 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 KW - Autonomes Fahrzeug KW - Mensch-Maschine-Kommunikation Y1 - 2024 UR - https://www.mdpi.com/2624-6511/7/1/18 U6 - https://doi.org/https://doi.org/10.3390/ smartcities7010018 VL - 7 IS - 1 SP - 460 EP - 474 ER -