Abstract: A system and method of planning external electric power usage of a vehicle traction battery includes retrieving, remotely from the vehicle, data including a current charge level of a traction battery in the vehicle and an estimated range of the vehicle based on the charge level. Input of planned power usage of the traction battery by a set of devices is input remotely from the vehicle. An estimated change of the current charge level is determined based upon the planned power usage based on the current charge level, the estimated range, and the planned power usage. Based on the estimated change of the current charge level, a revised charge level of the traction battery and a revised estimated range of the vehicle is determined, and the revised charge level of the traction battery and the revised estimated range of the vehicle is output to a user computing device.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor train a deep neural network based on plurality of real-world images, determine the accuracy of the deep neural network is below a threshold based on identifying one or more physical features by the deep neural network, including one or more object types, in the plurality of real-world images and generate a plurality of synthetic images based on the accuracy of the deep neural network is below a threshold based on identifying the one or more physical features using a photo-realistic image rendering software program and a generative adversarial network. The instructions can include further instructions to retrain the deep neural network based on the plurality of real-world images and the plurality of synthetic images and output the retrained deep neural network.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to train a deep neural network based on a plurality of real-world images, determine the accuracy of the deep neural network is below a threshold based on identifying one or more physical features by the deep neural network, including one or more object types, in the plurality of real-world images and generate a plurality of synthetic images based on the accuracy of the deep neural network is below a threshold based on identifying the one or more physical features using a photo-realistic image rendering software program and a generative adversarial network. The instructions can include further instructions to retrain the deep neural network based on the plurality of real-world images and the plurality of synthetic images and output the retrained deep neural network.