IntentNet: Learning to Predict Intention from Raw Sensor Data

Understand

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.