Technology-Independent Algorithm for Collision Warning System at Semi-Controlled Intersections
Bibliographic record
Abstract
Most of the collision warning systems that are available in the automotive market are mainly designed to detect imminent rear-end and/or lane-departure collisions. So far, no collision warning system is commercially available to detect imminent angle and turning collisions at semi-controlled intersections where the driver of a vehicle attempts to depart a minor road (controlled by a stop sign) to turn right, to turn left, or to cross an uncontrolled major road. One of the major causes for collisions at non-signalized intersections is the human error and misjudgment of the driver of the minor-road vehicle. Therefore, using a properly-designed collision warning system will have the potential to reduce, or even eliminate, this type of collision by reducing human error. This paper introduces a technology-independent algorithm for a collision warning system that can detect imminent collisions at semi-controlled intersections. The system utilizes commercially-available detectors to detect the approaching vehicles on the major road and calculate their speeds, accelerations, and rates of change of acceleration to estimate the time required to reach the intersection. The time required by the minor-road vehicle to clear the intersection is modeled as a function of driver and vehicle characteristics. By comparing the two times, the system displays a message for the driver of the minor-road vehicle when the departure maneuver is safe. An application example is provided to illustrate the proposed algorithm.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".