Real-Time Fully Unsupervised Domain Adaptation for Lane Detection in
  Autonomous Driving
Kshitij Bhardwaj Zishen Wan Arijit Raychowdhury Ryan Goldhahn
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.