aiqbench analysis
This study benchmarks Vision-Language Models for autonomous UAV navigation, identifying a critical trade-off between model scale and flight stability under temporal constraints. The research demonstrates that mid-weight architectures offer the optimal balance, mitigating both structural hallucinations in lightweight models and trajectory overcorrections caused by inference lag in heavyweight systems. This work provides essential empirical data on integrating large language models into real-time robotic control loops without compromising safety.
Stress-Query and Temporal Reliability of Vision-Language Models as copilots in Autonomous UAV Navigation
arXiv:2610.23877v1 Announce Type: new Abstract: Integrating Vision-Language Models (VLMs) into autonomous UAV navigation enables high-level semantic reasoning but introduces critical temporal bottlenecks that can destabilize flight control. This paper presents a systematic benchmark that evaluates the operational trade-offs among model scale, hardware constraints, and flight reliability. By subjecting the system to extensive stress tests on both a high-performance workstation and a constrained integrated-graphics laptop, we isolate the kinetic impact of inference latency against reasoning accuracy. Our findings reveal a performance dichotomy: lightweight models struggle with structural hallucinations that break deterministic state machines, while heavyweight models induce trajectory overcorrections due to inference lag. We demonstrate that a mid-weight architecture strikes the optimal balance, successfully rehabilitating navigation in degraded environments without compromising real-time stability across different hardware profiles.
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