Distracted Drivers Detection in Mixed Vehicle Platoons Using Velocity Measurements Only
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distracted drivers are a major factor in road safety that critically and continuously threaten the roads. While highly distracted drivers can be observed by surrounding vehicles, detecting moderate abnormalities such as delayed driver response is crucial for road safety and cannot be observed by the surrounding vehicles. The main challenge arises from the fact that normal human drivers’ behavior is unknown and difficult to be estimated. This study uses velocity output-only measurements available from sensors in mixed autonomous and human-driven platoons to detect low to moderately distracted human drivers within the same platoon. The output measurements are related mathematically to each other, which is known as transmissibility relations. Transmissibility is constructed and formulated to treat the unknown normal human behavior as an external factor that acts on the platoon. Thus, transmissibility becomes independent of the unknown human behavior and is then used to obtain an estimation of the human-driven vehicle’s velocity. Next, a residual-based technique is used between the estimated and measured velocities to detect abnormal driving behaviors. As an example of distracted drivers, we apply the proposed approach to a class of low to moderately-drunk drivers. The proposed approach is verified first numerically and then applied to a set of laboratory mobile robots.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it