Extracting Situations with Uneasy Driving in NDS-Data
Bibliographic record
Abstract
Different types of driver workload are suggested to impact driving performance. Operating a vehicle in a situation where the driver feel uneasy is one example of driver workload. In this study, passenger car driving data collected with Naturalistic Driving Study (NDS) data acquisition equipment was analyzed, aiming to identify situations corresponding to a high driver's subjective rating of ‘unease’. Data from an experimental study with subjects driving a passenger car in normal traffic was used. Situations were rated by the subjects according to experienced ‘unease’, and the Controller Area Network (CAN) data from the vehicle was used to describe the driving conditions and identify driving patterns corresponding to the situations rated as ‘uneasy’. These driving patterns were matched with the data in a NDS database and the method was validated using video data. Two data mining approaches were applied. The first was based on an ensemble classifier on general variables derived from the CAN-data to predict the subjective rating of segments of the data. The second used hierarchical clustering with a distance metric based on the principal variance components over segments. The ensemble classifier explained a large proportion of the variance when adjusting for driver and route. The hierarchical clustering method performed well, distinct clusters corresponding to a high driver subjective rating could be obtained. Identifying situations with increased driver workload in NDS data is a complex task addressing a large variation of traffic situations and driver experiences. The proposed method is a first approach to help address this topic using data mining.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".