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IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving
End-to-end autonomous driving has emerged as a compelling paradigm for learning planning directly from sensor observations, while recent world-model-based approaches further enrich this paradigm by enabling explicit reasoning about how the scene may evolve in the future. Yet future prediction alone
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