In various examples, a deep neural network (DNN) is trained—using image data alone—to
accurately predict distances to objects, obstacles, and/or a detected free-space boundary …

In various examples, a deep neural network (DNN) is trained to accurately predict, in
deployment, distances to objects and obstacles using image data alone. The DNN may be …

In various examples, a sequential deep neural network (DNN) may be trained using ground
truth data generated by correlating (eg, by cross-sensor fusion) sensor data with image data …

In various examples, a multi-sensor fusion machine learning model—such as a deep neural
network (DNN)—may be deployed to fuse data from a plurality of individual machine …

In various examples, a two-dimensional (2D) and three-dimensional (3D) deep neural
network (DNN) is implemented to fuse 2D and 3D object detection results for classifying …

Techniques for determining and/or predicting a trajectory of an object by using the
appearance of the object, as captured in an image, are discussed herein. Image data …

In various examples, a deep neural network (DNN) is trained for sensor blindness detection
using a region and context based approach. Using sensor data, the DNN may compute …

In various examples, systems and methods are disclosed that preserve rich, detail-centric
information from a real-world image by augmenting the real-world image with simulated …

In various examples, live perception from sensors of a vehicle may be leveraged to detect
and classify intersection contention areas in an environment of a vehicle in real-time or near …

In various examples, a deep neural network (DNN) may be used to detect and classify
animate objects and/or parts of an environment. The DNN may be trained using camera-to …