Analysis of Driving Behavior in Adverse Weather Conditions

• Published in: IEEE International Conference and Workshop in Óbuda on Electrical and Power Engineering (Online) pp. 255 - 262
• Main Authors: Raiyn, Jamal, Chaar, Mohamad Mofeed, Weidl, Galia
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.10.2024
• Subjects: Adaptive systems; Automobiles; Autonomous vehicles; Convolutional neural network; Convolutional neural networks; Feature extraction; Human factors; human factors. Driving behavior; Meteorology; naturalistic driving studies; Psychology; Road safety; safety-critical events; Vehicles
• ISSN: 2831-4506
• DOI: 10.1109/CANDO-EPE65072.2024.10772895

This paper discusses the impact of Connected Cooperative and Automated Mobility (CCAM) on safety-critical events. The replacement of human drivers by autonomous vehicles (A V s) 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 (A WCs).