  Accurate lane detection is critical for navigation in autonomous vehicles,
particularly the active lane which demarcates the single road space that the
vehicle is currently traveling on. Recent state-of-the-art lane detection
algorithms utilize convolutional neural networks (CNNs) to train deep learning
models on popular benchmarks such as TuSimple and CULane. While each of these
models works particularly well on train and test inputs obtained from the same
dataset, the performance drops significantly on unseen datasets of different
environments. In this paper, we present a real-time robust neural network
output enhancement for active lane detection (RONELD) method to identify,
track, and optimize active lanes from deep learning probability map outputs. We
first adaptively extract lane points from the probability map outputs, followed
by detecting curved and straight lanes before using weighted least squares
linear regression on straight lanes to fix broken lane edges resulting from
fragmentation of edge maps in real images. Lastly, we hypothesize true active
lanes through tracking preceding frames. Experimental results demonstrate an up
to two-fold increase in accuracy using RONELD on cross-dataset validation
tests.
