As deep learning models are deployed on resource constrained edge platforms in autonomous driving systems, reli able knowledge of hardware behavior under resource degradation becomes an essential requirement. Therefore, we introduce a systematic characterization of CPU load, GPU utilization, RAM consumption, power draw, throughput, and thermal behaviour of TensorRT-optimized YOLOv10s, YOLOv11s and YOLO2026n pipelines running on NVIDIA Jetson Nano under a large-scale fault injection campaign targeting both lane-following and ob ject detection tasks. Faults are synthesized using a decoupled framework that leverages large language models (LLMs) and latent diffusion models (LDMs), based on original data from our JetBot platform data collection. Results show that across both tasks and both models the inference engines keep GPU occupancy stable, temperature rise under control, and power consumption within safe limits, while memory usage settles into a consistent release pattern after the initial warm-up phase. Object detection tends to show somewhat more variability in memory and thermal behavior, yet both tasks point to the same conclusion: the TensorRT pipelines hold up well even when the input data is heavily degraded. These findings offer a hardware-level view of model reliability that sits alongside, rather than against, the broader body of work focused on inference performance at the edge.

翻译：随着深度学习模型在自动驾驶系统中资源受限的边缘平台上部署，对资源退化下硬件行为的可靠认知成为一项基本要求。因此，我们针对运行于NVIDIA Jetson Nano上的TensorRT优化YOLOv10s、YOLOv11s及YOLO2026n管道，在大规模故障注入实验（同时针对车道保持与目标检测任务）中，系统表征其CPU负载、GPU利用率、RAM消耗、功耗、吞吐量及热行为。故障利用一个解耦框架合成，该框架基于JetBot平台数据采集原始数据，结合大语言模型（LLMs）与潜在扩散模型（LDMs）。结果表明，在两个任务及所有模型中，推理引擎保持GPU占用率稳定、温度上升可控、功耗处于安全范围内，而内存使用在初始预热阶段后形成一致的释放模式。目标检测在内存与热行为方面表现出略高的变异性，但两个任务指向相同结论：即使输入数据严重退化，TensorRT管道仍能保持良好性能。这些发现提供了模型可靠性的硬件层面视角，与广泛关注边缘推理性能的研究成果相辅相成而非对立。