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 …