LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
4 Jul 2018
•
Ze Wang
•
Weiqiang Ren
•
Qiang Qiu
Lane detection is to detect lanes on the road and provide the accurate
location and shape of each lane. It severs as one of the key techniques to
enable modern assisted and autonomous driving systems...However, several unique
properties of lanes challenge the detection methods. The lack of distinctive
features makes lane detection algorithms tend to be confused by other objects
with similar local appearance. Moreover, the inconsistent number of lanes on a
road as well as diverse lane line patterns, e.g. solid, broken, single, double,
merging, and splitting lines further hamper the performance. In this paper, we
propose a deep neural network based method, named LaneNet, to break down the
lane detection into two stages: lane edge proposal and lane line localization. Stage one uses a lane edge proposal network for pixel-wise lane edge
classification, and the lane line localization network in stage two then
detects lane lines based on lane edge proposals. Please note that the goal of
our LaneNet is built to detect lane line only, which introduces more
difficulties on suppressing the false detections on the similar lane marks on
the road like arrows and characters. Despite all the difficulties, our lane
detection is shown to be robust to both highway and urban road scenarios method
without relying on any assumptions on the lane number or the lane line
patterns. The high running speed and low computational cost endow our LaneNet
the capability of being deployed on vehicle-based systems. Experiments validate
that our LaneNet consistently delivers outstanding performances on real world
traffic scenarios.(read more)