  One of the most critical pieces of the self-driving puzzle is the task of
predicting future movement of surrounding traffic actors, which allows the
autonomous vehicle to safely and effectively plan its future route in a complex
world. Recently, a number of algorithms have been proposed to address this
important problem, spurred by a growing interest of researchers from both
industry and academia. Methods based on top-down scene rasterization on one
side and Generative Adversarial Networks (GANs) on the other have shown to be
particularly successful, obtaining state-of-the-art accuracies on the task of
traffic movement prediction. In this paper we build upon these two directions
and propose a raster-based conditional GAN architecture, powered by a novel
differentiable rasterizer module at the input of the conditional discriminator
that maps generated trajectories into the raster space in a differentiable
manner. This simplifies the task for the discriminator as trajectories that are
not scene-compliant are easier to discern, and allows the gradients to flow
back forcing the generator to output better, more realistic trajectories. We
evaluated the proposed method on a large-scale, real-world data set, showing
that it outperforms state-of-the-art GAN-based baselines.
