A decoupled offline-online framework uses LLMs and latent diffusion models to generate fault scenarios for testing edge-based lane-following models, revealing large robustness drops under conditions like fog.
Visionfault-350k: A large-scale fault injection dataset for robotic vision systems
2 Pith papers cite this work. Polarity classification is still indexing.
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TensorRT YOLO pipelines on Jetson Nano keep GPU occupancy, power draw, and temperature stable even under heavy fault-injected inputs for object detection and lane following.
citing papers explorer
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LLM-Generated Fault Scenarios for Evaluating Perception-Driven Lane Following in Autonomous Edge Systems
A decoupled offline-online framework uses LLMs and latent diffusion models to generate fault scenarios for testing edge-based lane-following models, revealing large robustness drops under conditions like fog.
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Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection
TensorRT YOLO pipelines on Jetson Nano keep GPU occupancy, power draw, and temperature stable even under heavy fault-injected inputs for object detection and lane following.