  Predicting the future motion of traffic agents is crucial for safe and
efficient autonomous driving. To this end, we present PredictionNet, a deep
neural network (DNN) that predicts the motion of all surrounding traffic agents
together with the ego-vehicle's motion. All predictions are probabilistic and
are represented in a simple top-down rasterization that allows an arbitrary
number of agents. Conditioned on a multi-layer map with lane information, the
network outputs future positions, velocities, and backtrace vectors jointly for
all agents including the ego-vehicle in a single pass. Trajectories are then
extracted from the output. The network can be used to simulate realistic
traffic, and it produces competitive results on popular benchmarks. More
importantly, it has been used to successfully control a real-world vehicle for
hundreds of kilometers, by combining it with a motion planning/control
subsystem. The network runs faster than real-time on an embedded GPU, and the
system shows good generalization (across sensory modalities and locations) due
to the choice of input representation. Furthermore, we demonstrate that by
extending the DNN with reinforcement learning (RL), it can better handle rare
or unsafe events like aggressive maneuvers and crashes.

Robotics;Machine Learning