  In this paper, we explore the use of vehicle-to-vehicle (V2V) communication
to improve the perception and motion forecasting performance of self-driving
vehicles. By intelligently aggregating the information received from multiple
nearby vehicles, we can observe the same scene from different viewpoints. This
allows us to see through occlusions and detect actors at long range, where the
observations are very sparse or non-existent. We also show that our approach of
sending compressed deep feature map activations achieves high accuracy while
satisfying communication bandwidth requirements.
