1 research outputs found
Holistic Grid Fusion Based Stop Line Estimation
Intersection scenarios provide the most complex traffic situations in
Autonomous Driving and Driving Assistance Systems. Knowing where to stop in
advance in an intersection is an essential parameter in controlling the
longitudinal velocity of the vehicle. Most of the existing methods in
literature solely use cameras to detect stop lines, which is typically not
sufficient in terms of detection range. To address this issue, we propose a
method that takes advantage of fused multi-sensory data including stereo camera
and lidar as input and utilizes a carefully designed convolutional neural
network architecture to detect stop lines. Our experiments show that the
proposed approach can improve detection range compared to camera data alone,
works under heavy occlusion without observing the ground markings explicitly,
is able to predict stop lines for all lanes and allows detection at a distance
up to 50 meters.Comment: Submitted to ICPR202