Machine Learning Method for Road Vehicle Collected Data Analysis
Bibliographic record
Abstract
Abstract A major challenge encountered in the development of systems exposed to weather stressors, such as autonomous vehicles and unstaffed aerial vehicles, is to ensure their proper functioning under adverse rain or snow conditions. Since the sensing of the surroundings by these vehicles relies on optical sensors such as lidars and cameras, it is essential to ensure the robustness of these systems from the early stages of the project. In this respect, experiments in climatic wind tunnels provide a solution for simulating the operating conditions that the autonomous vehicles will confront. This work proposes a method based on field measurements and unsupervised machine learning to faithfully reproduce in controlled environments real weather conditions captured during wintertime in Ontario, Canada. The purpose of this paper is not to investigate correlations between observed weather conditions and the characteristics of the precipitation encountered, but rather to establish a consistent method based on outdoor disdrometer data to identify critical parameters to be simulated in climatic wind tunnels. To achieve this goal, weather data such as temperature, relative humidity, and droplet size distribution were recorded at General Motors’s McLaughlin Advanced Technology Track (MATT) using an FD70 disdrometer and WXT530 weather transmitter, both manufactured by Vaisala, installed on a car provided by the Automotive Center of Excellent (ACE) team of the University of Ontario Institute of Technology. The implementation of the proposed method allowed the identification of precipitation clusters characterized by parameters of a theoretical model for particle size distributions fitted to the collected data.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".