Automated Vehicles (AVs) are on the cusp of commercialization, prompting global governments to organize the forthcoming mobility phase. However, the advancement of technology alone cannot guarantee the successful commercialization of AVs without insi...
Advanced Driver Assistance Systems (ADAS) aim to automate transportation fully. A key part of this automation includes tasks such as traffic light detection and automatic braking. While indoor experiments are prevalent due to computational demands an...
To deepen the understanding of the impact of car-following driving style (CFDS) on traffic conflict risk and address the lack of clear CFDS evaluation metrics, this study proposes an improved CFDS metric based on the Asymmetric Behavior (AB) theory. ...
Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This rese...
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and ...
Aesthetics has always been an advanced requirement in road environment design, because it can provide a pleasant driving experience and guide better driving behavior for human drivers. However, it remains unknown whether aesthetics-based road environ...
Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on con...
This study addresses the issue of wrong-way driving (WWD) incidents at partial cloverleaf (parclo) interchange terminals in the United States. These incidents are a safety concern, often attributed to geometric design features and inadequate traffic ...
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.
The purpose of this study is to explore nonlinear and threshold effects of traffic statuses and road geometries, as well as their interactions, on traffic accident severity. In contrast to earlier research that primarily defined road alignment qualit...
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