IntentNet: Learning to Predict Intention from Raw Sensor Data
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 reducing reaction time in self-driving applications.
Cite
Text
Casas et al. "IntentNet: Learning to Predict Intention from Raw Sensor Data." Conference on Robot Learning, 2018.Markdown
[Casas et al. "IntentNet: Learning to Predict Intention from Raw Sensor Data." Conference on Robot Learning, 2018.](https://mlanthology.org/corl/2018/casas2018corl-intentnet/)BibTeX
@inproceedings{casas2018corl-intentnet,
title = {{IntentNet: Learning to Predict Intention from Raw Sensor Data}},
author = {Casas, Sergio and Luo, Wenjie and Urtasun, Raquel},
booktitle = {Conference on Robot Learning},
year = {2018},
pages = {947-956},
url = {https://mlanthology.org/corl/2018/casas2018corl-intentnet/}
}