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Multi-Task Machine-Learned Models for Object Intention Determination in Autonomous Driving

机译:多任务机器学习的自动驾驶目标意图确定模型

摘要

Generally, the disclosed systems and methods utilize multi-task machine-learned models for object intention determination in autonomous driving applications. For example, a computing system can receive sensor data obtained relative to an autonomous vehicle and map data associated with a surrounding geographic environment of the autonomous vehicle. The sensor data and map data can be provided as input to a machine-learned intent model. The computing system can receive a jointly determined prediction from the machine-learned intent model for multiple outputs including at least one detection output indicative of one or more objects detected within the surrounding environment of the autonomous vehicle, a first corresponding forecasting output descriptive of a trajectory indicative of an expected path of the one or more objects towards a goal location, and/or a second corresponding forecasting output descriptive of a discrete behavior intention determined from a predefined group of possible behavior intentions.
机译:通常,公开的系统和方法利用多任务机器学习的模型来确定自动驾驶应用中的目标意图。例如,计算系统可以接收相对于自动驾驶车辆获得的传感器数据以及与自动驾驶车辆的周围地理环境相关联的地图数据。可以将传感器数据和地图数据作为输入提供给机器学习的意图模型。该计算系统可以从机器学习的意图模型接收针对多个输出的联合确定的预测,该多个输出包括至少一个检测输出,该检测输出指示在自动驾驶车辆的周围环境内检测到的一个或多个物体,第一对应的预测输出描述了轨迹。指示一个或多个对象朝向目标位置的预期路径,和/或描述从预定义的可能行为意图组中确定的离散行为意图的第二相应预测输出。

著录项

  • 公开/公告号US2019382007A1

    专利类型

  • 公开/公告日2019-12-19

    原文格式PDF

  • 申请/专利权人 UBER TECHNOLOGIES INC.;

    申请/专利号US201916420686

  • 发明设计人 SERGIO CASAS;WENJIE LUO;RAQUEL URTASUN;

    申请日2019-05-23

  • 分类号B60W30/095;G05D1/02;G06K9;G06N20;

  • 国家 US

  • 入库时间 2022-08-21 11:24:27

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