Qualcomm Incorporated

Varun Ravi Kumar

Senthil Kumar Yogamani

Debasmit Das

Venkatraman Narayanan

Sweta Priyadarshi

Shivansh Rao

A method includes receiving one or more images, wherein at least one of the one or more images depicts a water region and analyzing, by one or more processors, the one or more images using a first machine learning model to determine a depth of the water region. The method also includes analyzing, by the one or more processors, the one or more images using a second machine learning model to determine a surface normal of the water region and performing, by the one or more processors, using a third machine learning model, multi-class segmentation of the one or more images. Additionally, the method includes performing one or more fusion operations on outputs of at least two of the first machine learning model, the second machine learning model and the third machine learning model to generate a classification of the water region.

Free space detection for parking and driving in puddle areas with permuted fusion network

Systems and techniques are described herein for training an object-detection model. For instance, a method for training an object-detection model is provided. The method may include obtaining a light detection and ranging (LIDAR) capture; obtaining a first LIDAR-based representation of an object as captured from a first distance; obtaining a second LIDAR-based representation of the object as captured from a second distance; augmenting the LIDAR capture using the first LIDAR-based representation of the object and the second LIDAR-based representation of the object to generate an augmented LIDAR capture; and training a machine-learning object-detection model using the augmented LIDAR capture.

Adaptive region-based object sampling for object detection

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving, by a processor, image data from a camera image sensor; receiving, by the processor, point cloud data from a light detection and ranging (LiDAR) sensor; generating, by the processor and using a first machine learning model, fused image data that combines the image data and the point cloud data; and determining, by the processor and using a second machine learning model, whether the fused image data satisfies a criteria based on whether a population risk function of the first machine learning model exceeds a threshold. Other aspects and features are also claimed and described.

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San Diego, CA, United States of America

Varun Ravi Kumar