The lane detection systems in ADAS and self-driving
applications need to be resilient to adverse weather like rain,
to minimize the chance of serious accidents. However, it is
still challenging to maintain high precision in these conditions.
State-of-the-art segmentation-based detectors use a naive postprocessing method to quickly extract lane points from the
segmented output based on the local maxima, but it is prone
to failure when the segmentation contains errors. In this paper,
we present an Adaptive Lane point EXtractor (ALEX) which
overcomes this limitation by harnessing statistical properties
from local regions of the segmentation to implicitly identify and
compensate for errors. ALEX merges both local maximum and
local mean statistics with CNN attributes to build a holistic hybrid
feature set. Lane points are predicted from this representation
by estimating their location on a reduced-size confidence map. A
lateral offset is predicted to compute the precise location on the
full-sized scene, while a class label prediction denotes which lane
class the point belongs to. Besides the segmentation output and
lane point ground truth, no additional information is required
by ALEX, making it effectively agnostic to weather conditions
and segmentation methods. Evaluation on two publicly available
clear-weather databases and one rain-translated database verified
that our approach consistently improved accuracy while reducing
false positive and false negative rates, regardless of weather or
segmentation method.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done