  Inspired by the UNet architecture of semantic image segmentation, we propose
a lightweight UNet using depthwise separable convolutions (DSUNet) for
end-to-end learning of lane detection and path prediction (PP) in autonomous
driving. We also design and integrate a PP algorithm with convolutional neural
network (CNN) to form a simulation model (CNN-PP) that can be used to assess
CNN's performance qualitatively, quantitatively, and dynamically in a host
agent car driving along with other agents all in a real-time autonomous manner.
DSUNet is 5.16x lighter in model size and 1.61x faster in inference than UNet.
DSUNet-PP outperforms UNet-PP in mean average errors of predicted curvature and
lateral offset for path planning in dynamic simulation. DSUNet-PP outperforms a
modified UNet in lateral error, which is tested in a real car on real road.
These results show that DSUNet is efficient and effective for lane detection
and path prediction in autonomous driving.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Robotics