While deep neural networks are being utilized heavily for autonomous driving, they need to be adapted to new unseen environmental conditions for which they were not trained. We focus on a safety critical application of lane detection, and propose a lightweight, fully unsupervised, real-time adaptation approach that only adapts the batch-normalization parameters of the model. We demonstrate that our technique can perform inference, followed by on-device adaptation, under a tight constraint of 30 FPS on Nvidia Jetson Orin. It shows similar accuracy (avg. of 92.19%) as a state-of-the-art semi-supervised adaptation algorithm but which does not support real-time adaptation.

翻译：尽管深度神经网络被广泛应用于自动驾驶，但它们需要适应未训练过的全新环境条件。我们聚焦于车道检测这一安全关键应用，提出了一种轻量级、完全无监督的实时自适应方法，该方法仅调整模型的批量归一化参数。实验证明，我们的技术可在Nvidia Jetson Orin平台以30 FPS的严格约束下完成推理和设备端自适应。该方法展现出与当前最先进的半监督自适应算法相似的精度（平均92.19%），但后者无法支持实时自适应。