In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative ofa sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.
在各种示例中,可以使用通过将传感器数据与表示图像序列的图像数据相关联(例如,通过交叉传感器融合)生成的地面实况数据来训练顺序深度神经网络(DNN)。在部署中,顺序DNN可以利用传感器相关性来仅使用图像数据计算各种预测。预测可以包括自我车辆的视场中的对象在世界空间中的速度、图像空间中的对象的当前和未来位置,和/或对象物体与自我车辆之间的碰撞时间(TTC)。这些预测可以用作感知系统的一部分来理解和响应自我车辆的当前物理环境。
Temporal information prediction in autonomous machine applications
自主机器应用中的时间信息预测
2020-09-22
Patent
Electronic Resource
Chinese
IPC: | G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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