Under a standardized evaluation on the public MMDAP dataset, a simple random forest method (88% accuracy, AUC 0.85) outperforms re-implementations of three published vehicle-dynamics drowsiness detectors, whose reported metrics are largely not reproducible.
Detecting Driver’s Drowsi- ness Using Multiwavelet Packet Energy Spectrum,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Comparison of Lightweight Methods for Vehicle Dynamics-Based Driver Drowsiness Detection
Under a standardized evaluation on the public MMDAP dataset, a simple random forest method (88% accuracy, AUC 0.85) outperforms re-implementations of three published vehicle-dynamics drowsiness detectors, whose reported metrics are largely not reproducible.