  Recent research on automotive driving developed an efficient end-to-end
learning mode that directly maps visual input to control commands. However, it
models distinct driving variations in a single network, which increases
learning complexity and is less adaptive for modular integration. In this
paper, we re-investigate human's driving style and propose to learn an
intermediate driving intention region to relax difficulties in end-to-end
approach. The intention region follows both road structure in image and
direction towards goal in public route planner, which addresses visual
variations only and figures out where to go without conventional precise
localization. Then the learned visual intention is projected on vehicle local
coordinate and fused with reliable obstacle perception to render a navigation
score map widely used for motion planning. The core of the proposed system is a
weakly-supervised cGAN-LSTM model trained to learn driving intention from human
demonstration. The adversarial loss learns from limited demonstration data with
one local planned route and enables reasoning of multi-modal behavior with
diverse routes while testing. Comprehensive experiments are conducted with
real-world datasets. Results show the proposed paradigm can produce more
consistent motion commands with human demonstration, and indicates better
reliability and robustness to environment change.
