Cooperative Estimation of Road Condition Based on Dynamic Consensus and Vehicular Communication
Why this work is in the frame
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Bibliographic record
Abstract
In the presence of measurement noises and potential sensor malfunctioning, road condition identification by a single vehicle may not be reliable for motion planning and control of autonomous/intelligent vehicles. In this paper, we propose a distributed cooperative road condition estimation scheme for vehicular networks, involving a dynamic consensus algorithm to increase the reliability and accuracy of estimation. In this scheme, each vehicle individually estimates the road condition parameter using an online recursive least squares estimator, and disseminates it through the network to fuse the individual estimates through a consensus algorithm. It is shown that the proposed scheme well adapts to the variations in the road condition, improves the road condition estimation accuracy even with limited number of vehicles, and reduces the sensitivity to measurement noises. Simulation results demonstrate that estimation of the road condition using the proposed scheme improves the performance of maneuver planning for collision avoidance in slippery road conditions.
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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