At the Toyota Research Institute we are following the one-system-two-modes approach to building truly automated cars. More precisely, we simultaneously aim for the L4/L5 chauffeur application and the the guardian system, which can be considered as a highly advanced driver assistance system of the future that prevents the driver from making any mistakes. TRI aims to equip more and more consumer vehicles with guardian technology and in this way to turn the entire Toyota fleet into a giant data collection system. To leverage the resulting data advantage, TRI performs substantial research in machine learning and, in addition to supervised methods, particularly focuses on unsupervised and self-supervised approaches. In this presentation, I will present recent results regarding (self-)supervised methods for perception problems in the context of automated driving.
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