  Following detection and tracking of traffic actors, prediction of their
future motion is the next critical component of a self-driving vehicle (SDV)
technology, allowing the SDV to operate safely and efficiently in its
environment. This is particularly important when it comes to vulnerable road
users (VRUs), such as pedestrians and bicyclists. These actors need to be
handled with special care due to an increased risk of injury, as well as the
fact that their behavior is less predictable than that of motorized actors. To
address this issue, in the current study we present a deep learning-based
method for predicting VRU movement, where we rasterize high-definition maps and
actor's surroundings into a bird's-eye view image used as an input to deep
convolutional networks. In addition, we propose a fast architecture suitable
for real-time inference, and perform an ablation study of various rasterization
approaches to find the optimal choice for accurate prediction. The results
strongly indicate benefits of using the proposed approach for motion prediction
of VRUs, both in terms of accuracy and latency.
