Title: Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision,
Abstract: Learning to drive faithfully in highly stochastic urban settings remains an
open problem. To that end, we propose a Multi-task Learning from Demonstration
(MT-LfD) framework which uses supervised auxiliary task prediction to guide the
main task of predicting the driving commands. Our framework involves an
end-to-end trainable network for imitating the expert demonstrator's driving
commands. The network intermediately predicts visual affordances and action
primitives through direct supervision which provide the aforementioned
auxiliary supervised guidance. We demonstrate that such joint learning and
supervised guidance facilitates hierarchical task decomposition, assisting the
agent to learn faster, achieve better driving performance and increases
transparency of the otherwise black-box end-to-end network. We run our
experiments to validate the MT-LfD framework in CARLA, an open-source urban
driving simulator. We introduce multiple non-player agents in CARLA and induce
temporal noise in them for realistic stochasticity.