Abstract: A method of processing image data includes receiving, with a frame correction machine-learning (ML) model executing on processing circuitry, an image frame captured from a first camera of a plurality of cameras; performing, with the frame correction ML model executing on the processing circuitry, image frame correction to generate a corrected image frame based on weights or biases of the frame correction ML model applied to two or more of: samples of the image frame, samples of previously captured image frames from the first camera, or samples from image frames from other cameras of the plurality of cameras; and performing, with the processing circuitry, post-processing based on the corrected image frame.

Abstract: Systems and techniques are described herein for determining road profiles. For instance, a method for determining a road profiles is provided. The method may include extracting image features from one or more images of an environment, wherein the environment includes a road; generating a segmentation mask based on the image features; determining a subset of the image features based on the segmentation mask; generating image-based three-dimensional features based on the subset of the image features; obtaining point-cloud-based three-dimensional features derived from a point cloud representative of the environment; combining the image-based three-dimensional features and the point-cloud-based three-dimensional features to generate combined three-dimensional features; and generating a road profile based on the combined three-dimensional features.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method is provided that includes generating a top view image of an object using a plurality of images captured from different views. The method involves determining portions of the images that depict the object and generating novel views of the object from at least one novel view not present within the plurality of images. Corresponding portions containing an occluded view and an unobstructed view of the object are identified and corrected views for occluded views are determined based on corresponding unobstructed views using a machine learning model. A top view image may be then generated based on the corrected views. The invention enables improved visibility for autonomous driving systems in situations where objects are occluded or partially obstructed. Other aspects and features are also claimed and described.

Abstract: A method for accelerating machine learning on a computing device is described. The method includes partitioning neural network parameters and input data processed by a plurality of multiply-accumulate (MAC) units of a MAC array of the computing device. The method also includes interleaving MAC operations on the neural network parameters and the input data accessed according to a data sliding window and/or a stride N to compute an output during each cycle, in which N is greater than or equal to one.