Abstract: The described aspects and implementations enable efficient detection and classification of objects with machine learning models that deploy a bird's-eye view representation and are trained using depth ground truth data. In one implementation, disclosed are system and techniques that include obtaining images, generating, using a first neural network (NN), feature vectors (FVs) and depth distributions pixels of images, wherein the first NN is trained using training images and a depth ground truth data for the training images. The techniques further include obtaining a feature tensor (FT) in view of the FVs and the depth distributions, and processing the obtained FTs, using a second NN, to identify one or more objects depicted in the images.

Abstract: Systems, methods, computer-readable media, techniques, and methodologies are disclosed for generating vehicle controls and/or driving policies based on machine learning models that utilize intermediate representation of driving scenes as well as demonstrations (e.g. by behavioral cloning). An intermediate representation that includes inductive biases about the structure of driving scenes for a vehicle can be generated by a self-supervised first machine learning model. A driving policy for the vehicle can be determined by a second machine learning model trained by a set of expert demonstrations and based on the intermediate representation. The expert demonstrations can include labelled data. An appropriate vehicle action may then be determined based on the driving policy. A control signal indicative of this vehicle action may then be output to cause an autonomous vehicle, for example, to implement the appropriate vehicle action.