IntentNet: Learning to Predict Intention from Raw Sensor Data

Sergio Casas; Wenjie Luo; Raquel Urtasun

CoRL 20180 citations

IntentNet: Learning to Predict Intention from Raw Sensor Data

Sergio Casas, Wenjie Luo, Raquel Urtasun

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.

BibTeX

@inproceedings{corl2018_intentnetlearnin,
  title = {IntentNet: Learning to Predict Intention from Raw Sensor Data},
  author = {Sergio Casas and Wenjie Luo and Raquel Urtasun},
  booktitle = {CoRL 2018},
  year = {2018}
}