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Autonomous Vehicles Featuring Machine-Learned Yield Model

机译:自动车辆以机器学习的产量模型为特色

摘要

The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.
机译:本公开提供了包括或以其他方式利用机器学习的产量模型的自主车辆系统和方法。 具体地,可以训练机器学习的产量模型或以其他方式被配置为接收和处理自主车辆和/或周围环境所感知的对象的特征数据,并且响应于收到特征数据,提供产生的产生决策 自动车辆相对于物体。 例如,第一对象的产量决定可以描述自治车辆相对于第一对象的屈服行为(例如,屈服于第一对象或不产生到第一对象)。 示例对象包括交通信号,附加车辆或其他对象。 可以根据机器学习的产量模型提供的产生决定来控制自主车辆的运动。

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