Sep 2026· Proceedings of the Human Factors and Ergonomics Society Annual Meeting· 0 citations· 5 references
TL;DR
A five-state driver impairment classification system using late fusion of ECG-derived heart rate variability and facial action unit (AU) analysis is presented and the Human Identity and Autonomy Gap framework predicts that anger monitoring will elicit greater psychological reactance than monitoring of functional states.
Abstract
Current driver monitoring systems detect drowsiness and distraction but not anger, even though aggressive driving accounts for 56% of fatal crashes (AAA Foundation for Traffic Safety, 2016). This paper presents a five-state driver impairment classification system using late fusion of ECG-derived heart rate variability and facial action unit (AU) analysis. Three contributions are reported. First, an operational pipeline using BIOPAC MP150 ECG and OpenFace 2.0, integrated with a fixed base driving simulator, is fully implemented. Second, Monte Carlo simulation (
N
= 200 agents; 500 replications; pilot
n
= 24) projects that Bayesian late fusion achieves
F
1 = 0.85 for anger detection, a +0.13 gain over the best unimodal system, with graceful degradation under noise. Third, the Human Identity and Autonomy Gap (HIAG) framework predicts that a planned companion transparency study (
N
= 120) will find that anger monitoring will elicit greater psychological reactance than monitoring of functional states.
Multimodal classification often suffers from recognition reliability that is asymmetric across data sources and classes, and its evaluation is frequently complicated by information leakage from overlapping sampling windows. This paper proposes a class-wise-optimized reliability fusion model (CORF), using the classifica...
Yi Tian, Jian-Ping Hu, Wen Dong et al.· Symmetry· 0 citations
Electroencephalography (EEG)-based Brain–Computer Interfaces offer direct insight into a driver’s mental state, relevant to autonomous-vehicle perception stacks. This work addresses vigilance/drowsiness specifically, one of several driver states relevant to Level 3 takeover readiness. Detecting drowsiness before handov...
Sirine Ammar, M. Karray, Mohamed Ksantini· Electronics· 0 citations
Reliable risk assessment remains a central challenge for Autonomous Vehicles (AVs). Despite advances in automation, passenger cognition provides a non-intrusive auxiliary signal that improves both objective and perceived safety without requiring active human intervention. We introduce an Electroencephalogram (EEG)-base...
Yingkai Yang, Ashton Yu Xuan Tan, Bo-Wen Li et al.· 0 citations
With the continuous growth in motor vehicle ownership, traffic safety risks caused by dangerous driving behaviors remain an important concern. This paper presents RMDD, a Raspberry Pi-based multimodal dangerous driving monitoring feasibility prototype using YOLO26 visual models, personalized facial fatigue estimation,...
Road safety remains a critical challenge worldwide, with approximately 1.19 million fatalities annually attributed to traffic accidents, often due to human error. As we advance towards higher levels of vehicle automation, challenges still exist, as driving with automation can cognitively over-demand drivers if engaging...
Ji-Yao Wang, Xiao Yang, Zhen-Yu Wang et al.· IEEE transactions on intelli...· 0 citations
SAE Level 3 automated driving systems assume full control of the dynamic driving task within their operational design domain. Ensuring a safe and timely transition from automated to manual driving therefore necessitates continuous monitoring and assessment of driver state. This review presents a synthesis of 85 studies...
Hao-Ran Wu, Timothy Tettey Nartey, Guang-Jun Wan et al.· PLoS ONE· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
