  We propose an end-to-end three-task convolutional neural network (3TCNN)
having two regression branches of bounding boxes and Hu moments and one
classification branch of object masks for lane detection and road recognition.
The Hu-moment regressor performs lane localization and road guidance using
local and global Hu moments of segmented lane objects, respectively. Based on
3TCNN, we then propose lateral offset and path prediction (PP) algorithms to
form an integrated model (3TCNN-PP) that can predict driving path with dynamic
estimation of lane centerline and path curvature for real-time autonomous
driving. We also develop a CNN-PP simulator that can be used to train a CNN by
real or artificial traffic images, test it by artificial images, quantify its
dynamic errors, and visualize its qualitative performance. Simulation results
show that 3TCNN-PP is comparable to related CNNs and better than a previous
CNN-PP, respectively. The code, annotated data, and simulation videos of this
work can be found on our website for further research on NN-PP algorithms of
autonomous driving.
