SAVANT reformulates semantic anomaly detection as layered consistency verification, raising VLM recall by 18.5% on real driving images and enabling a fine-tuned 7B open model to reach 90.8% recall and 93.8% accuracy.
LLM4AD: Large language models for autonomous driving: Concept, review, benchmark, experiments, and future trends
4 Pith papers cite this work. Polarity classification is still indexing.
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SteinsGateDrive decouples LLM inference latency from vehicle control by pre-selecting alpha, beta, and gamma worldline futures that a runtime validates against safety contracts until abort conditions trigger.
SwarmDrive uses local SLMs on vehicles for event-triggered semantic V2V intent sharing and consensus, improving occluded intersection success from 68.9% to 94.1% and cutting latency to 151.4 ms in a 5-seed simulation.
ManeuverGPT shows an LLM agent framework that generates parameters for successful J-turn maneuvers across vehicle models in simulation using prompt-based iteration and a validator agent.
citing papers explorer
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Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning
SAVANT reformulates semantic anomaly detection as layered consistency verification, raising VLM recall by 18.5% on real driving images and enabling a fine-tuned 7B open model to reach 90.8% recall and 93.8% accuracy.
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Steins;Gate Drive: Semantic Safety Arbitration over Structured Futures for Latency-Decoupled LLM Planning
SteinsGateDrive decouples LLM inference latency from vehicle control by pre-selecting alpha, beta, and gamma worldline futures that a runtime validates against safety contracts until abort conditions trigger.
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SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving
SwarmDrive uses local SLMs on vehicles for event-triggered semantic V2V intent sharing and consensus, improving occluded intersection success from 68.9% to 94.1% and cutting latency to 151.4 ms in a 5-seed simulation.
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ManeuverGPT Agentic Control for Safe Autonomous Stunt Maneuvers
ManeuverGPT shows an LLM agent framework that generates parameters for successful J-turn maneuvers across vehicle models in simulation using prompt-based iteration and a validator agent.