Identifying Driver Behavior in Preturning Maneuvers Using In-Vehicle CANbus Signals
Bibliographic record
Abstract
Our objective in this contribution is to categorize driver behavior in terms of preturning maneuvers. We analyze driving behavior in an urban environment prior to turns using data obtained from the CANbus of an instrumented vehicle during a one-hour driving period for 12 different individuals. CANbus data streams such as vehicle speed, gas pedal pressure, brake pedal pressure, steering wheel angle, and acceleration are collected and analyzed for 5, 10, and 15 seconds of driving prior to each turn. We consider all turns for each driver and extract statistical features from the signals and use cluster analysis to categorize drivers into groups reflecting different driving styles. The results show that using this approach we can effectively cluster drivers into two groups. The results show consistency in the membership within a cluster throughout the different timeframes. We conclude that driver behavior classification from such data streams is possible and we hope in the near future to devise driver descriptors that include additional maneuvers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".