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.
@article{arxiv.2101.07907,
title = {IntentNet: Learning to Predict Intention from Raw Sensor Data},
author = {Sergio Casas and Wenjie Luo and Raquel Urtasun},
journal= {arXiv preprint arXiv:2101.07907},
year = {2021}
}