Next-Generation Vehicle Platooning: Leveraging Quantum Long Short-Term Memory Networks
Bibliographic record
Abstract
This study explores the integration of Quantum Long Short Term Memory (QLSTM) networks into vehicle platooning systems to enhance the coordination and performance of platoons on highways. Vehicle platooning is a transformative approach to managing fleets of vehicles by utilizing advanced communication and control technologies. These technologies synchronize speed and maintain optimal inter-vehicle distances, improving traffic flow and safety. Our research presents a novel system model for vehicle platooning that includes both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications enabled by a Roadside Unit (RSU), which facilitates the exchange of critical data such as speed, position, and control inputs among vehicles. The core of our methodology is the application of a QLSTM network, which is trained with historical traffic data to predict future states of vehicle platoons. This allows platoons to dynamically adjust their behavior in response to real-time conditions, optimizing the overall traffic flow and reducing the likelihood of collisions. The objective function of our model focuses on minimizing deviations from desired state references, emphasizing the importance of maintaining specified inter-vehicle distances and velocities. Preliminary results demonstrate that our QLSTM-enhanced platooning model significantly improves the stability and efficiency of vehicular platoons, particularly in complex traffic scenarios on the I–26 freeway in South Carolina. This study not only provides a practical framework for implementing quantum computing techniques in real-world transportation systems but also opens new avenues for further research in quantum-resilient traffic management solutions.
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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 | 0.000 | 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 | 0.000 | 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".