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Planning in massive digital twins with point-cloud data becomes significantly faster by connecting belief states using a $W_2$-metric adaptation of RRT*, reusing measurement information to avoid repeated propagation.
State-of-the-art autonomous driving algorithms falter on the new Boreas Road Trip dataset, revealing their tendency to overfit to simpler driving environments.
