IntentNet: Learning to Predict Intention from Raw Sensor Data

• URL: http://arxiv.org/abs/2101.07907v1
• Date: Wed, 20 Jan 2021 00:31:52 GMT
• Title: IntentNet: Learning to Predict Intention from Raw Sensor Data
• Authors: Sergio Casas, Wenjie Luo, Raquel Urtasun
• Abstract summary: 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.
• Score: 86.74403297781039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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