Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multim...
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...
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...
The rapid advancement of automotive technologies has spurred the development of innovative applications within intelligent transportation systems (ITS), aimed at enhancing safety, efficiency and sustainability. These applications, such as advanced dr...
Artificial intelligence (AI) has promoted application and development of self-driving cars. However, when self-driving cars encounter ethical dilemma, it is still hard to make a satisficing and clear decision-making by these present moral rules and m...
Metro drivers are more likely to trigger accidents if they suffer from cognitive distractions during manual driving. However, identifying metro drivers' cognitive distractions faces challenges as generally no obvious behavior can be found during the ...
Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection...