Short term predictions of preceding vehicle speeds for connected and automated vehicles
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
The pursuit of fully automated driving has been a significant point of interest among researchers and industrial communities alike in recent years. As instantly replacing every vehicle with its automated counterpart is implausible, autonomous vehicles need to operate alongside human driven ones. Human drivers tend to show a lot of variation in their driving behaviour, and making non-optimal decisions is a frequent practice. This chaotic environment makes it difficult for controllers on-board automated vehicles to make optimal decisions. Given advanced control techniques, such as model predictive controllers, that can make use of a valid prediction of other traffic participants' behaviours for a significant performance boost, a method to successfully make such predictions will become appealing. In this paper, considering a host connected and automated vehicle or CAV, the performance of a recurrent neural network is investigated for this task using some standard driving cycles data to predict the velocity of the preceding vehicle for multiple horizons in urban driving scenarios.
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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 | 0.000 |
| 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 it