  This paper investigates how end-to-end driving models can be improved to
drive more accurately and human-like. To tackle the first issue we exploit
semantic and visual maps from HERE Technologies and augment the existing
Drive360 dataset with such. The maps are used in an attention mechanism that
promotes segmentation confidence masks, thus focusing the network on semantic
classes in the image that are important for the current driving situation.
Human-like driving is achieved using adversarial learning, by not only
minimizing the imitation loss with respect to the human driver but by further
defining a discriminator, that forces the driving model to produce action
sequences that are human-like. Our models are trained and evaluated on the
Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world
driving data. Extensive experiments show that our driving models are more
accurate and behave more human-like than previous methods.
