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

Real-Time Fully Unsupervised Domain Adaptation for Lane Detection in Autonomous Driving