Abstract: In various examples, a signal processing pipeline is dynamically generated or instantiated for a signal processing request. To generate the pipeline, a graph topology—including nodes and edges—may be created to represent features, functionality, and characteristics of a signal processing system. The nodes, representing processing tasks, may be connected via edges having associated costs for performing, by a node, a processing task on an output of a prior or edge-connected node. For a given signal processing request, the nodes or processing tasks to be included may be selected and, using a graph routing algorithm and the costs between and among the determined nodes, a path through the nodes may be determined—thereby defining, at least in part, the signal processing pipeline.

Abstract: In various examples, live perception from wide-view sensors may be leveraged to detect features in an environment of a vehicle. Sensor data generated by the sensors may be adjusted to represent a virtual field of view different from an actual field of view of the sensor, and the sensor data—with or without virtual adjustment—may be applied to a stereographic projection algorithm to generate a projected image. The projected image may then be applied to a machine learning model—such as a deep neural network (DNN)—to detect and/or classify features or objects represented therein. In some examples, the machine learning model may be pre-trained on training sensor data generated by a sensor having a field of view less than the wide-view sensor such that the virtual adjustment and/or projection algorithm may update the sensor data to be suitable for accurate processing by the pre-trained machine learning model.

Abstract: In various examples, sensor data may be adjusted to represent a virtual field of view different from an actual field of view of the sensor, and the sensor data—with or without virtual adjustment—may be applied to a stereographic projection algorithm to generate a projected image. The projected image may then be applied to a machine learning model—such as a deep neural network (DNN)—to detect and/or classify features or objects represented therein.

Abstract: In various examples, a signal processing pipeline is dynamically generated or instantiated for a signal processing request. To generate the pipeline, a graph topology—including nodes and edges—may be created to represent features, functionality, and characteristics of a signal processing system. The nodes, representing processing tasks, may be connected via edges having associated costs for performing, by a node, a processing task on an output of a prior or edge-connected node. For a given signal processing request, the nodes or processing tasks to be included may be selected and, using a graph routing algorithm and the costs between and among the determined nodes, a path through the nodes may be determined—thereby defining, at least in part, the signal processing pipeline.

Abstract: In various examples, a signal processing pipeline is dynamically generated or instantiated for a signal processing request. To generate the pipeline, a graph topology—including nodes and edges—may be created to represent features, functionality, and characteristics of a signal processing system. The nodes, representing processing tasks, may be connected via edges having associated costs for performing, by a node, a processing task on an output of a prior or edge-connected node. For a given signal processing request, the nodes or processing tasks to be included may be selected and, using a graph routing algorithm and the costs between and among the determined nodes, a path through the nodes may be determined—thereby defining, at least in part, the signal processing pipeline.

Abstract: In various examples, live perception from wide-view sensors may be leveraged to detect features in an environment of a vehicle. Sensor data generated by the sensors may be adjusted to represent a virtual field of view different from an actual field of view of the sensor, and the sensor data—with or without virtual adjustment—may be applied to a stereographic projection algorithm to generate a projected image. The projected image may then be applied to a machine learning model—such as a deep neural network (DNN)—to detect and/or classify features or objects represented therein. In some examples, the machine learning model may be pre-trained on training sensor data generated by a sensor having a field of view less than the wide-view sensor such that the virtual adjustment and/or projection algorithm may update the sensor data to be suitable for accurate processing by the pre-trained machine learning model.

Abstract: In various examples, a signal processing pipeline is dynamically generated or instantiated for a signal processing request. To generate the pipeline, a graph topology—including nodes and edges—may be created to represent features, functionality, and characteristics of a signal processing system. The nodes, representing processing tasks, may be connected via edges having associated costs for performing, by a node, a processing task on an output of a prior or edge-connected node. For a given signal processing request, the nodes or processing tasks to be included may be selected and, using a graph routing algorithm and the costs between and among the determined nodes, a path through the nodes may be determined—thereby defining, at least in part, the signal processing pipeline.