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Closed-Loop Aware Planning via Imitation-Free Speed Scoring for End-to-End Autonomous Driving
End-to-end autonomous driving has predominantly relied on open-loop imitation learning for training, which presents two limitations. First, expert demonstrations show only one behavior per scenario, so the model cannot learn alternative valid behaviors and, in turn, cannot adapt to user-specified pr
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