KLOTSKI: Towards Consensus Enabled Collaborative Vehicles in Intelligent Transportation
Bibliographic record
Abstract
With the fast development of Vehicle to Everything (V2X) and vehicular automation, vehicles communicate with infrastructure and each other to enable cooperative decision-making, fostering advancements in autonomous driving and intelligent transportation systems. However, coordinating vehicular decisions in general transportation scenarios like lane change and intersection traffic has not been systematically explored so far. To address this gap, this paper investigates cooperated vehicle systems that utilize V2X technology to align decision-making in transportation. To this end, we identify the central challenge in vehicular coordination as the “consensus” problem in transportation, allowing vehicles to reach an agreement on a global schedule and then safely execute their actions. Furthermore, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Klotski</small>, a framework to enable vehicles to achieve consensus by considering challenges such as latency and jitter of V2X communication, vehicle faults, and dynamic participants. To better address these challenges, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Klotski</small> framework employs a modular design comprising three core components: the Membership Management Module, Intelligent Decision Module, and Coordination Module. Furthermore, we illustrate the practical application of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Klotski</small> in two real transportation scenarios while highlighting its desired properties through security analysis. We build a prototype of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Klotski</small> and evaluate its performance based on discrete event simulations. The evaluation results show that, without considering crashes, our scheme can reach consensus 27 times per minute, while participant crashes increase the consensus time by an average of 80.5%.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".