Driver Distraction Detection in Extreme Conditions Using Kolmogorov–Arnold Networks - Hollósi, János; Kovács, Gábor; Sysyn, Mykola; Kurhan, Dmytro; Fischer, Szabolcs; Nagy, Viktor (MDPI, 2025)
ENG: Driver distraction can have severe safety consequences, particularly in public transportation. This paper presents a novel approach for detecting bus driver actions, such as mobile phone usage and interactions with passengers, using Kolmogorov–Arnold networks (KANs). The adversarial FGSM attack method was applied to assess the robustness of KANs in extreme driving conditions, like adverse weather, high-traffic situations, and bad visibility conditions. In this research, a custom dataset was used in collaboration with a partner company in the field of public transportation. This allows the efficiency of Kolmogorov–Arnold network solutions to be verified using real data. The results suggest that KANs can enhance driver distraction detection under challenging conditions, with improved resilience against adversarial attacks, particularly in low-complexity networks.
Keywords: driver monitoring system; road safety; artificial intelligence; neural network; Kolmogorov–Arnold networks (KANs); driver distraction detection; КТІ; TECHNOLOGY; TECHNOLOGY::Information technology