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Recent research on automotive driving developed an efficient end-to-endlearning mode that directly maps visual input to control commands. However, itmodels distinct driving variations in a single network, which increaseslearning complexity and is less adaptive for modular integration. In thispaper, we re-investigate human's driving style and propose to learn anintermediate driving intention region to relax difficulties in end-to-endapproach. The intention region follows both road structure in image anddirection towards goal in public route planner, which addresses visualvariations only and figures out where to go without conventional preciselocalization. Then the learned visual intention is projected on vehicle localcoordinate and fused with reliable obstacle perception to render a navigationscore map widely used for motion planning. The core of the proposed system is aweakly-supervised cGAN-LSTM model trained to learn driving intention from humandemonstration. The adversarial loss learns from limited demonstration data withone local planned route and enables reasoning of multi-modal behavior withdiverse routes while testing. Comprehensive experiments are conducted withreal-world datasets. Results show the proposed paradigm can produce moreconsistent motion commands with human demonstration, and indicates betterreliability and robustness to environment change.

DeepGoal: Learning to Drive with driving intention from Human Control Demonstration