Abstract: In various examples, lane location criteria and object class criteria may be used to determine a set of objects in an environment to track. For example, lane information, freespace information, and/or object detection information may be used to filter out or discard non-essential objects (e.g., objects that are not in an ego-lane or adjacent lanes) from objects detected using an object detection algorithm. Further, objects corresponding to non-essential object classes may be filtered out to generate a final filtered set of objects to be tracked that may be of a lower quantity than the actual number of detected objects. As a result, object tracking may only be executed on the final filtered set of objects, thereby decreasing compute requirements and runtime of the system without sacrificing object tracking accuracy and reliability with respect to more pertinent objects.

Abstract: In various examples, lane location criteria and object class criteria may be used to determine a set of objects in an environment to track. For example, lane information, freespace information, and/or object detection information may be used to filter out or discard non-essential objects (e.g., objects that are not in an ego-lane or adjacent lanes) from objects detected using an object detection algorithm. Further, objects corresponding to non-essential object classes may be filtered out to generate a final filtered set of objects to be tracked that may be of a lower quantity than the actual number of detected objects. As a result, object tracking may only be executed on the final filtered set of objects, thereby decreasing compute requirements and runtime of the system without sacrificing object tracking accuracy and reliability with respect to more pertinent objects.

Abstract: In various examples, lane location criteria and object class criteria may be used to determine a set of objects in an environment to track. For example, lane information, freespace information, and/or object detection information may be used to filter out or discard non-essential objects (e.g., objects that are not in an ego-lane or adjacent lanes) from objects detected using an object detection algorithm. Further, objects corresponding to non-essential object classes may be filtered out to generate a final filtered set of objects to be tracked that may be of a lower quantity than the actual number of detected objects. As a result, object tracking may only be executed on the final filtered set of objects, thereby decreasing compute requirements and runtime of the system without sacrificing object tracking accuracy and reliability with respect to more pertinent objects.

Abstract: When a computing system is running at a lower clock rate, in response to an event that triggers the computing system to increase the clock rate, a list of threads pending execution by the computing system is accessed. The list includes a thread that, when executed, causes the clock rate to increase. That thread is selected and executed before any other thread in the list is executed.

Abstract: When a computing system is running at a lower clock rate, in response to an event that triggers the computing system to increase the clock rate, a list of threads pending execution by the computing system is accessed. The list includes a thread that, when executed, causes the clock rate to increase. That thread is selected and executed before any other thread in the list is executed.

Abstract: One embodiment provides a method to wake an electronic device having a central processing unit (CPU) from an idle condition. The method includes creating a worker queue in an interrupt-request (IRQ) driver module of the operating-system kernel of the device, receiving in the kernel an indication of user input in a form of an IRQ, and in response to receiving the indication of user input, posting a request in the worker queue to boost clock speed in the CPU. The request is then processed, causing an increase in the clock speed.