Leveraging Multiagent Learning for Automated Vehicles Scheduling at Nonsignalized Intersections
Bibliographic record
Abstract
Recent advancements of Vehicle-to-Everything (V2X) communication combined with artificial intelligence (AI) technologies have shown enormous potentials for improving traffic management efficiency and intelligence. To provide innovative and effective data-driven traffic management solution for the coming automated vehicle era, we present a vehicle-road collaboration-enabled nonsignalized intersection management architecture in this paper. First, by dividing the intersection zone into the central section (CS) and the waiting section (WS), a vehicle regulation scheme involved with communication and computation planes is developed for V2X-enabled nonsignalized intersection management. Specifically, in order to guarantee vehicle safety, the definition of no overlapping occupation time in CS and the fastest crossing time point (FCTP) algorithm are employed for vehicle collision avoidance. Second, considering the relative coordination between adjacent intersections, a multiagent-based deep reinforcement learning scheduling (MA-DRLS) algorithm is proposed to realize cooperative multiple intersection management. Through information exchange with different intersection agents, each agent can obtain an optimal scheduling strategy using independent deep reinforcement learning (DRL) network. The features of fixed Q-targets and experience replay are leveraged to improve the reliability of neural network during the training process. Finally, simulation performances in terms of intersection throughput and vehicle waiting time have been provided to validate the effectiveness and demonstrate the superiority of the proposed nonsignalized intersection management solution.
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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) | 0.001 | 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".