arXiv:2609.13756v1 Announce Type: new Abstract: One in every five vehicle accidents on the road today is caused simply due to driver fatigue. Fatigue or otherwise drowsiness, significantly reduces th...
GraTED is a Graph–Tensor EEG Decoding Model that preserves the spatial and temporal structure of passenger electroencephalography (EEG) data by modeling electrode positions as an undirected graph and applying graph convolution and low‑rank tensor classification. It is designed to predict risk perception and identify danger in autonomous‑vehicle scenarios. On the PassengerEEG dataset, GraTED outperforms seven baseline methods in balanced accuracy while using the fewest learnable parameters.
arXiv:2607. 16760v1 Announce Type: cross Abstract: Driver monitoring systems (DMS) increasingly rely on facial cues to infer drowsiness, distraction, and cognitive load in real time.
Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affective computing are largely limited to visual modalities and rarely include conversational information, making it difficult to capture the linguistic and interactive cues underlying driver emotion.
arXiv:2607. 14683v1 Announce Type: new Abstract: Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction.
The paper investigates the use of thermal infrared video to non‑invasively monitor cardiorespiratory and sudomotor activity in industrial human‑machine interfaces. It presents a signal‑processing pipeline that tracks facial regions, aggregates thermal signals, and separates slow sudomotor trends from faster heart‑rate and breathing‑rate components. Experiments on 31 driver‑monitoring sessions show that thermal imaging can estimate heart rate, breathing rate, and electrodermal activity with reasonable accuracy, while highlighting challenges such as ROI selection, polarity changes, latency, and subject variability.
arXiv:2604. 08435v2 Announce Type: replace-cross Abstract: It remains challenging to assess driver fatigue from untrimmed videos under constrained computational budgets, due to the difficulty of modeling long-range temporal dependencies in subtle facial expressions.
arXiv:2609.08038v2 Announce Type: cross Abstract: Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as fa...
arXiv:2606. 05149v1 Announce Type: cross Abstract: Vehicle body type is a significant determinant of cyclist injury severity in overtaking crashes, yet automated tools for classifying vehicles into injury-risk-relevant categories from naturalistic roadway video do not exist in the open literature.
arXiv:2609.07128v1 Announce Type: new Abstract: Reliable risk assessment remains a central challenge for Autonomous Vehicles (AVs). Despite advances in automation, passenger cognition provides a non-...
arXiv:2606. 26922v1 Announce Type: cross Abstract: Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states.
arXiv:2607. 25570v1 Announce Type: cross Abstract: The development of autonomous vehicles (AVs) usually relies heavily on data-driven artificial intelligence (AI) models that require large volumes of sensor data with ground-truth annotations.