IntentNet：从原始传感器数据学习预测意图
IntentNet: Learning to Predict Intention from Raw Sensor Data
摘要：
为了规划安全的机动，自动驾驶汽车需要了解其他交通参与者的意图。我们将意图定义为离散的高级行为以及描述未来运动的连续轨迹的组合。在本文中，我们开发了一个单阶段的检测器和预报器，利用激光雷达传感器产生的三维点云以及环境的动态地图。我们的多任务模型在节省计算的同时比各自的独立模块实现了更好的准确性，这对于减少自动驾驶应用中的反应时间至关重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
