Multiagent Collaborative Decision-Making Using Small Vision–Language Models for Autonomous Driving
Bibliographic record
Abstract
Autonomous vehicles face challenges in complex environments due to the computational inefficiency of large language models (LLMs) and the lack of multi-agent collaboration in existing decision-making approaches. This paper proposes a small Vision-Language Model (VLM)-based framework for connected autonomous vehicles (CAVs), balancing computational efficiency with robust reasoning and perception. The framework integrates a perception module with mapping and annotation layers to provide visual and contextual information. To enhance decision-making, we introduce Domain-Augmented Chain-of-Thought (DACoT), enabling context-aware planning and negotiation for multi-agent coordination. In intersection scenarios, DACoT improves Success Steps (SS) by 28.9% and Pass Rate (PR) by 24.2% over CoT, and SS by 57.2% and PR by 48.2% over Direct prompting. In highway merging, DACoT achieves 19.6% higher SS and 99.97% higher PR than CoT. These results highlight DACoT’s adaptability in complex multi-agent interactions, establishing an efficient, scalable, and collaborative decision-making framework for real-time autonomous driving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".