Abstract: Techniques for training multiple resolution deep neural networks (DNNs) for vehicle autonomous driving comprise obtaining a training dataset for training a plurality of DNNs for an autonomous driving feature of the vehicle, sub-sampling the training dataset to obtain a plurality of training datasets comprising the training dataset and one or more sub-sampled datasets each having a different resolution than a remainder of the plurality of training datasets, training the plurality of DNNs using the plurality of training datasets, respectively, determining a plurality of outputs for the autonomous driving feature using the plurality of trained DNNs and the input data, receiving input data for the autonomous driving feature captured by a sensor device, and determining a best output for the autonomous driving feature using the plurality of outputs.

Abstract: Techniques for training multiple resolution deep neural networks (DNNs) for vehicle autonomous driving comprise obtaining a training dataset for training a plurality of DNNs for an autonomous driving feature of the vehicle, sub-sampling the training dataset to obtain a plurality of training datasets comprising the training dataset and one or more sub-sampled datasets each having a different resolution than a remainder of the plurality of training datasets, training the plurality of DNNs using the plurality of training datasets, respectively, determining a plurality of outputs for the autonomous driving feature using the plurality of trained DNNs and the input data, receiving input data for the autonomous driving feature captured by a sensor device, and determining a best output for the autonomous driving feature using the plurality of outputs.

Abstract: An autonomous driving technique comprises determining an image quality metric for each image frame of a series of image frames of a scene outside of a vehicle captured by a camera system and determining an image quality threshold based on the image quality metrics for the series of image frames. The technique then determines whether the image quality metric for a current image frame satisfies the image quality threshold. When the image quality metric for the current image frame satisfies the image quality threshold, object detection is performed by at least utilizing a first deep neural network (DNN) with the current image frame. When the image quality metric for the current image frame fails to satisfy the image quality threshold, object detection is performed by utilizing a second, different DNN with the information captured by another sensor system and without utilizing the first DNN or the current image frame.

Abstract: A battery management system for a vehicle having an electrically powered motor that is powered by a plurality of battery cells includes an electrical system for providing voltage and current from the battery cells to an electrical motor. A control is operable to at least one of (a) cause fuses of the electrical system to be the weakest link in the electrical system only during a failure event, (b) disconnect the battery cells from the battery management system only during a failure event, (c) separate the driving of balancing resistors into first and second stages, with the first stage comprising cell balancing control and the second stage comprising cell balancing with reverse voltage protection and (d) provide single stage reverse voltage protection to limit or effectively eliminate an electrical conduction path through a low impedance balancing circuit.