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Trajectory scoring can be dramatically improved by leveraging a dataset that better captures decision boundaries, leading to enhanced performance in autonomous driving systems.
Direct predictions in driving world models can lead to substantial inaccuracies in counterfactual scenarios, revealing a critical gap in current methodologies.
SCL's promise falters in the real world, but dynamically adapting training data to each test instance can bridge the gap.
