Non-cooperating vehicle tracking in VANETs using the conditional logit model
Bibliographic record
Abstract
Vehicular Ad Hoc Networks (VANETs) are widely considered as indispensable elements of the future intelligent transportation systems that are aiming to apply information and communications technologies to improve transportation safety and quality of experience. We present our take on a relatively unexplored problem, exploiting VANETs for on-road surveillance. The proposal is inspired by multi-agent systems intended for surveillance, e.g., a distributed camera network. We propose a tracking system composed of three operational modules, namely, localization, tracking data collection and prediction of future locations of a target. Camera equipped onboard units (OBUs) act as remote mobile sensors. Tracking messages are communicated among the OBUs and roadside units (RSUs). These messages are also triggered in the possible locations of the target in a timely manner. Therefore, it is imperative to scope the search to limit the number of OBUs and RSUs involved in the tracking operation, thus, minimizing the number of tracking messages. To this end, a movement modeling technique utilizes the OBU-observations to classify the target's movement pattern to aid future trajectory prediction. In our previous work, we proposed a Dirichlet-multinomial (D-M) model under the Bayesian estimation framework. In this paper, we present newly identified cues towards improving the movement estimation model. The D-M model is constrained to the assumption that all the choice sets are identical across trials. We demonstrate that this is almost never the case. The improved model exploits a choice model, called the conditional logit. The conditional logit model is attractive when choice sets vary across trials. Additionally, we weight outcome of each trial according to the given choice sets to achieve higher estimation accuracy. We evaluate the new model by means of an experimental analysis and compare results with the D-M model.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".