Surrogate safety measures as aid to driver assistance system design of the cognitive vehicle
Bibliographic record
Abstract
The driver assistance system part of the cognitive vehicle design can prevent rear, lateral and other collisions by using a collision warning system that integrates intelligent technology and human factors. To be effective, such a system should be able to analyse driving states including driver distraction and driver intent, assess the likelihood of collisions by working with surrogate safety measures and issue warnings to the driver. This study presents a longitudinal and lateral collision warning model that allows the inclusion of key surrogate safety measures such as distance between vehicles in longitudinal vehicle‐following mode or envelopes of vehicles in the lateral direction during lane migration/change/merge movements. The model can take into account values of driver distraction and driver intent variables obtained on‐line or from off‐line devices. The formulation is also applicable to time‐to‐crash surrogate safety measure. A pattern recognition method is used for the identification of pre‐crash condition while minimising false alarms. The surrogate safety model is presented and illustrative examples are provided. The surrogate safety measure‐based warning system is mainly intended for on‐line use in actual driving conditions. In addition, it can be used in driving simulators or for off‐line safety studies in association with microsimulators of traffic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".