Researchers at the University of California (UC), Berkeley, have recently developed a generative model that can predict the sequential motions of pairs of interacting agents, including self-driving vehicles as well as vehicles with human drivers. Their method, outlined in a paper pre-published on arXiv, is interpretable, which means that it can explain the logic behind its predictions, leading to greater reliability and generalizability.
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An interpretable model to predict the sequential motions of interacting agents
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