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.

通过提出的多任务学习框架，其中使用监督辅助任务预测来指导预测驾驶命令的主要任务，实现了灵敏度和表现性能的提高，同时还增加了模型的透明度。通过在 CARLA 模拟器中的实验验证了该框架的效果。