PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking
Why this work is in the frame
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Bibliographic record
Abstract
With the advent of autonomous vehicles, in particular its adaptability to harsh conditions, the research and development of autonomous vehicles attract significant attention by not only academia but also practitioners. Due to the high risk, high cost, and difficulty to test autonomous vehicles under harsh conditions, the hardware-in-the-loop (HIL) scaled platform has been proposed as it is a safe, inexpensive, and effective test method. This platform system consists of scaled autonomous vehicle, scaled roadway, monitoring center, transmission device, positioning device, and computers. This paper uses a case of the development process of tracking control for high-speed U-turn to build the tracking control function. Further, a simplified vehicle dynamics model and a trajectory tracking algorithm have been considered to build the simulation test. The experiment results demonstrate the effectiveness of the HIL scaled platform.
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.
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 | 0.001 |
| 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