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 - Chaar, Mohamad Mofeed A1 - Weidl, Galia T1 - Analysis of Driving Behavior in Adverse Weather Conditions T2 - IEEE 7th International Conference AND workshop in Óbuda on Electrical and Power Engineering (CANDO EPE 2024) N2 - 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). KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Wetter Y1 - 2024 UR - https://www.researchgate.net/publication/385591980_ANALYSIS_OF_DRIVING_BEHAVIOR_IN_ADVERSE_WEATHER_CONDITIONS ER - TY - INPR A1 - Chaar, Mohamad Mofeed A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Improving the Perception of Objects under Foggy Conditions in the Surrounding Environment N2 - 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. KW - Autonomes Fahrzeug KW - Wetter KW - Sicht Y1 - 2024 U6 - https://doi.org/https://doi.org/10.21203/rs.3.rs-3821656/v1 PB - Research Square Platform LLC ER -