  Behavior prediction of traffic actors is an essential component of any
real-world self-driving system. Actors' long-term behaviors tend to be governed
by their interactions with other actors or traffic elements (traffic lights,
stop signs) in the scene. To capture this highly complex structure of
interactions, we propose to use a hybrid graph whose nodes represent both the
traffic actors as well as the static and dynamic traffic elements present in
the scene. The different modes of temporal interaction (e.g., stopping and
going) among actors and traffic elements are explicitly modeled by graph edges.
This explicit reasoning about discrete interaction types not only helps in
predicting future motion, but also enhances the interpretability of the model,
which is important for safety-critical applications such as autonomous driving.
We predict actors' trajectories and interaction types using a graph neural
network, which is trained in a semi-supervised manner. We show that our
proposed model, TrafficGraphNet, achieves state-of-the-art trajectory
prediction accuracy while maintaining a high level of interpretability.

Machine Learning;Computer Vision and Pattern Recognition;Robotics;Machine Learning