Abstract
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 reduce reaction time in self-driving applications.
Authors
Sergio Casas, Wenjie Luo, Raquel Urtasun
Conference
CORL 2018
Full Paper
‘IntentNet: Learning to Predict Intention from Raw Sensor Data’ (PDF)
Uber ATG
