Detection of Intoxicated Drivers Using Online System Identification of Steering Behavior
Bibliographic record
Abstract
Impaired driving is known to be among the leading causes of death and injury on roads; however, the existing measures to address this menace appear to be insufficient. This paper presents a novel method to detect intoxicated driving and lays a foundation that can be implemented in future cars to derive personalized models of drivers and to detect not only intoxicated driving but also other reckless driving styles. We employ system identification techniques to develop models for sober and impaired drivers. A total of 200 sets of data from various subject drivers were collected in a high-fidelity driving simulator. The lateral preview error and the steering wheel angle were considered the input and output of a driver, respectively. We will demonstrate that the autoregressive noise integration moving average with exogenous input (ARIMAX) model best fits the data to describe the steering behavior of drivers. The positions of model poles are shown to be a good indicator of intoxicated driving behavior. An aggressive driving style due to impaired driving leads to the migration of dominant poles toward the instability region. The Kalman filter and online identification techniques are used to update the driver model during driving. The poles of this updated model are used for the detection of impaired driving.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".