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Safety-constrained and runtime-assured LLM multi-agent decision making for intelligent vehicle systems

Author

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  • Wei Dang
  • Lijun Gao

Abstract

Large language models offer flexible semantic reasoning for intelligent-vehicle interfaces, but probabilistic outputs, multi-agent disagreement, and provider failures complicate safety-sensitive use. We developed SCI-Proposed-v1.0, a lightweight multi-agent semantic decision architecture that combines transparent risk-informed routing with an independent Safety Validator and deterministic local Runtime Fallback. The system produces canonical semantic actions rather than trajectories or direct vehicle-control commands. In a frozen confirmatory benchmark of 150 unseen Base Scenarios, each represented by three reviewed language variants, SCI-Proposed completed 360/450 inputs (80.0%), compared with 169/450 (37.6%) for EI-MAS and 155/450 (34.4%) for a Single LLM. Language variants were treated as repeated observations within each Base Scenario, and all methods used the same frozen model configuration. Base-scenario paired differences were 0.4244 (95% confidence interval, 0.3489–0.5000; p

Suggested Citation

  • Wei Dang & Lijun Gao, 2026. "Safety-constrained and runtime-assured LLM multi-agent decision making for intelligent vehicle systems," PLOS ONE, Public Library of Science, vol. 21(10), pages 1-28, October.
    • Handle: RePEc:plo:pone00:0357928
    DOI: 10.1371/journal.pone.0357928
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