UTA RLDD: Real-Life Drowsiness Detection Dataset

A dataset for detecting driver drowsiness, published on Kaggle. The specific data collection method, size, and time range are not detailed in the available metadata. The author and organization are unknown.

Use Cases

• Train a classifier to detect drowsy driving states from sensor data (inferred from domain, verify after download)
• Benchmark real-time alert systems for driver fatigue (inferred from domain, verify after download)
• Analyze physiological or behavioral patterns preceding drowsiness events (inferred from domain, verify after download)

Strengths

• Published on Kaggle, a major platform for data science resources.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license are unknown, which may limit suitability assessment.

Provenance

Source

Kaggle