  Predicting the motion of agents such as pedestrians or human-driven vehicles
is one of the most critical problems in the autonomous driving domain. The
overall safety of driving and the comfort of a passenger directly depend on its
successful solution. The motion prediction problem also remains one of the most
challenging problems in autonomous driving engineering, mainly due to high
variance of the possible agent's future behavior given a situation. The two
phenomena responsible for the said variance are the multimodality caused by the
uncertainty of the agent's intent (e.g., turn right or move forward) and
uncertainty in the realization of a given intent (e.g., which lane to turn
into). To be useful within a real-time autonomous driving pipeline, a motion
prediction system must provide efficient ways to describe and quantify this
uncertainty, such as computing posterior modes and their probabilities or
estimating density at the point corresponding to a given trajectory. It also
should not put substantial density on physically impossible trajectories, as
they can confuse the system processing the predictions. In this paper, we
introduce the PRANK method, which satisfies these requirements. PRANK takes
rasterized bird-eye images of agent's surroundings as an input and extracts
features of the scene with a convolutional neural network. It then produces the
conditional distribution of agent's trajectories plausible in the given scene.
The key contribution of PRANK is a way to represent that distribution using
nearest-neighbor methods in latent trajectory space, which allows for efficient
inference in real time. We evaluate PRANK on the in-house and Argoverse
datasets, where it shows competitive results.
