In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state and risk level for real-time risk prediction, which is crucial for traffic safety. However, existing ...
Traffic crashes result from complex interactions between driver, roadway, and environmental factors, which traditional methods often fail to capture. This paper investigates the influence of road, weather, and socioeconomic factors on traffic crashes...
Left-turn slip lanes, also known as channelised right-turn lanes in right-hand driving countries, are widely implemented to facilitate left-turning at signalised intersections. However, pedestrian safety on slip lanes is not well known. At unsignalis...
OBJECTIVES: Understanding drivers' cognitive load is essential for enhancing road safety, as cognitive demands fluctuate across different driving scenarios, potentially impacting performance, and safety, particularly for drivers with neurological dis...
Driver drowsiness detection systems are crucial for road safety. However, existing machine learning models struggle to adjust thresholds for Skin Conductance (SC) adaptively signals due to insufficient feature extraction of tonic and phasic responses...
In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intenti...
Driver drowsiness is a leading cause of road accidents, resulting in significant societal, economic, and emotional losses. This paper introduces a novel and robust deep learning-based framework for real-time driver drowsiness detection, leveraging st...
Driver gaze estimation is important for various driver gaze applications such as building advanced driving assistance systems and understanding driver gaze behavior. Gaze estimation in terms of gaze zone classification requires large-scale labeled da...
Existing research on human-automated vehicle (AV) interactions has largely focused on auditory explanations, with less attention to how voice characteristics shape user trust. This paper explores the influence of gender similarity between users and A...
Studies in health technology and informatics
Apr 8, 2025
Identifying driver inattention is crucial for road safety, driver well-being and can be enhanced using multimodal physiological signals. However, effective fusion of multimodal data is highly challenging, particularly with intermediate fusion, where ...
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