In various examples, past location information and map information corresponding to an active person in an environment may be applied to a deep neural network (DNN), such as a recurrent neural network (RNN), trained to calculate information corresponding to a future trajectory of the active person. The output of the DNN may include, for each future time slice trained to predict the DNN, a confidence map representing a confidence of each pixel in which the active person is present, and a vector field representing a location of the active person in the confidence map of the previous time slice. The vector field may thus be used to track an object through a confidence map for each future time slice to generate a predicted future trajectory for each actor. In addition to the tracked past trajectory, the predicted future trajectory may also be used to generate a complete trajectory for an active person who may assist in the own vehicle while navigating the environment.
在各个示例中,对应于环境中的活动者的过去位置信息和地图信息可被应用于被训练成计算对应于活动者的未来轨迹的信息的深度神经网络(DNN)(诸如循环神经网络(RNN))。DNN的输出可包括,针对DNN被训练以预测的每个未来时间片的表示活动者存在的每个像素的置信度的置信度图以及表示活动者在先前时间片的置信度图中的位置的向量场。向量场因此可被用于通过每个未来时间片的置信度图来追踪对象,以生成每个活动者的预测的未来轨迹。除了追踪的过去轨迹之外,预测的未来轨迹还可用于为可在导航环境时帮助自车的活动者生成完整轨迹。
Future trajectory prediction in multi-actor environment for autonomous machine applications
用于自主机器应用的多活动者环境中的未来轨迹预测
2022-05-06
Patent
Elektronische Ressource
Chinesisch
IPC: | G06V / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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