  In order to plan a safe maneuver, self-driving vehicles need to understand
the intent of other traffic participants. We define intent as a combination of
discrete high-level behaviors as well as continuous trajectories describing
future motion. In this paper, we develop a one-stage detector and forecaster
that exploits both 3D point clouds produced by a LiDAR sensor as well as
dynamic maps of the environment. Our multi-task model achieves better accuracy
than the respective separate modules while saving computation, which is
critical to reducing reaction time in self-driving applications.
