Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electroencephalogram (EEG)-based detection systems. Due to the high-dimensional nature of EEG signals and ...
The widespread deployment of intelligent vehicles necessitates comprehensive testing across diverse driving scenarios. A significant challenge is generating critical testing scenarios to accurately evaluate vehicle performance. To overcome the limita...
Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people's life. The identification o...
Interactions between vehicle-pedestrian at intersections often lead to safety-critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the dev...
Driver drowsiness remains a critical factor in road safety, necessitating the development of robust detection methodologies. This study presents a dual-framework approach that integrates a convolutional neural network (CNN) and a facial landmark anal...
As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample resear...
INTRODUCTION: Predicting and interpreting crash severity is essential for developing cost-effective safety measures. Machine learning (ML) models in crash severity studies have attracted much attention recently due to their promising predicted perfor...
The safety of pedestrians in urban transportation systems has emerged as a significant research topic. As a vulnerable group within this transportation framework, pedestrians encounter heightened safety risks in complex urban road environments. Prote...
Recently, real-time traffic conflict prediction has drawn increasing attention due to its significant potential in proactive traffic safety systems. While various statistical and machine learning models have been developed for conflict prediction, tr...
The built environment exerts a significant influence on the frequency and severity of traffic accidents. Spatially uniform assumptions on the impacts of built environment factors commonly employed in existing research may lead to inconsistent and con...
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