A Real-World Testbed for V2X in Autonomous Vehicles: From Simulation to Actual Road Testing
Bibliographic record
Abstract
This paper proposes an edge computing-enhanced testbed for Vehicle-to-Everything (V2X) communication, using a scaled autonomous vehicle model. Addressing the limitations of traditional vehicle testing and simulations, our approach utilizes an F1Tenth scale vehicle to replicate real-world traffic scenarios, with a focus on non-line-of-sight and pedestrian-occluded challenges. The testbed incorporates a Road Side Unit (RSU) for edge-based data processing, allowing rapid data acquisition and transmission. Our controlled experiments assess the vehicle's response to V2X communication in complex environments specifically in NLOS scenarios such as occluded pedestrians, highlighting the potential of edge computing to augment autonomous vehicle perception and decision-making. Performance evaluation shows that the vehicle can effectively handle emergencies, achieving an end-to-end response time of less than 146 ms on an edge device. Compared to other popular autonomy frameworks, which rely on higher computational units unsuitable for deployment on edge devices. This study advances V2X technology in edge computing contexts for autonomous vehicles and paves the way for future research in robust, safe, and reliable autonomous systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".