Using GPS-based Data Acquisition to Evaluate Vehicle and Driver Performance
Bibliographic record
Abstract
GPS systems, a common aerospace application, have become widely available over the last ten years. Most data acquisition systems used for flight or ground testing of vehicles in the past were very expensive and hard to setup. This paper looks at the recently available low cost GPS-based systems for ground vehicle data acquisition and investigates their ability to evaluate vehicle and driver performance. An SCCA Spec Miata racecar was used at two different race tracks to evaluate the system. The data acquisition system acquires GPS position and calculates speed; in addition the system uses internal accelerometers to record lateral and longitudinal acceleration. Speed versus location on the track plots were most effective evaluating driver performance. Both longitudinal acceleration and velocity/time plots were effective determining acceleration, braking, and the effect of shift points on acceleration. Lateral acceleration was used to compare cornering forces and friction circle transitions from braking/acceleration. Race track segment analysis was used to measure changes to driving line through complex corners on the track in order to determine the optimal driving line. The effect of late braking was measured and determined to have minimal effect on overall lap times. It was concluded that GPS-based data acquisition systems are easy to use and with proper data reduction can provide accurate vehicle and driver performance measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".