Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. In recent years, there have been advances to handle more complex forms of navigational instruction. However, these networks are still trained on biased human driving data (yielding biased models), and are unable to capture the full distribution of possible actions that could be taken. By learning a set of unsupervised latent variables that characterize the training data, we present an online debiasing algorithm for autonomous driving. Additionally, we extend end-to-end driving networks with the ability to drive with purpose and perform point-to-point navigation. We formulate how our model can be used to also localize the robot according to correspondences between the map and the observed visual road topology, inspired by the rough localization that human drivers can perform, even in cases where GPS is noisy or removed all together. Our results highlight the importance of bridging the benefits from end-to-end learning with classical probabilistic reasoning and Bayesian inference to push the boundaries of autonomous driving.
Alexander Amini (MIT)
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