JETROS: A Scalable Robotic Testbed for Experimental Evaluation of Algorithms for Cooperative Vehicle Platooning
Bibliographic record
Abstract
This paper introduces JETROS, a modular and scalable robotic testbed for experimental evaluation of platooning strategies in autonomous vehicles. Each robot combines an NVIDIA Jetson Orin Nano board with a comprehensive sensor suite for implementing vision-based perception, and includes wireless interfaces for inter-vehicle communication. The integrated architecture provides a robust infrastructure for deploying and testing software-based strategies for addressing open challenges in coordinated platooning. Experiments using a leader-follower pair provide preliminary validation of core platooning functionalities, including lane detection, lane keeping, and control of intervehicle distance using layered PID controllers. Furthermore, the tested scenario leverages inter-vehicle communication for reference velocity sharing, as a way to improve safety in scenarios where sensor reliability is degraded, like when the cars move along curved paths. Availability of a high-performance embedded computing unit in each robot facilitates deployment and testing of sophisticated software stacks for perception, control, and communication. We expect that the flexibility and accessibility of the platform will promote experimental validation and critical examination of reported solutions for advanced platooning strategies, ultimately supporting the transition from simulation to real-world deployment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".