A Driver Activity Dataset with Multiple RGB-D Cameras and mmWave Radars
Bibliographic record
Abstract
Driver activity recognition has become crucial for intelligent transportation and automotive safety systems. However, existing studies mainly focus on fatigue-related behaviors while neglecting other activities for analyzing driver behavior and intent. In this work, we introduce a novel dataset for fine-grained driver activities, utilizing diverse sensors such as mmWave radars, RGB, and depth cameras, each of which includes three camera angles: body, face, and hands. This multi-modal and multi-angle approach allows for comprehensive driver behavior analysis, including hand gestures, head movement, and object interactions. Moreover, including mmWave radars provides significant privacy advantages, as the sparse dynamic point clouds prevent the identification of the driver's face and other personal information. This dataset is valuable for researchers and developers on driver activity recognition and behavior analysis. It enables the development and evaluation of robust, privacy-conscious solutions for improving road safety, driver assistance, and in-vehicle interaction. Furthermore, the multi-modal nature of the data enables the exploration of sensor fusion techniques, unlocking the full potential of diverse sensing modalities to understand complex driver behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".