Quickest Detection of Abnormal Vehicle Movements on Highways
Bibliographic record
Abstract
The quest to develop self-driving vehicles remain an active research topic. A self-driving vehicle on a highway employs numerous sensors to track the state of its surrounding vehicles. Considering the proximity of surrounding vehicles, it is critical to detect their unusual maneuvers as quickly as possible, especially when autonomous vehicles operate among human-operated traffic. In this paper, we present an approach to quickly detect lane-changing maneuvers of a nearby vehicle. The proposed algorithm is based on the optimal likelihood ratio test, known as Page test. The proposed approach is presented in the form of a novel process model to be employed by autonomous vehicles. In addition to traditional states, such as position and velocity, the proposed process model adds two additional states of a surrounding vehicle being monitored: the lane-index (LIDX) and the lane-change-index (LcIDX). Then we present an approach to keep these two indices up to date in the quickest possible manner through the proposed Page test based 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 | 0.000 | 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".