Autonomous driving policies with strong closed-loop performance frequently lack timely internal predictions of surrounding vehicles during near-collision events, and causal correction of prediction errors leads to improved ego planning.
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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DeepSight uses parallel latent feature prediction in BEV for long-horizon world modeling and adaptive text reasoning to reach state-of-the-art closed-loop performance on the Bench2drive benchmark.
citing papers explorer
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What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
Autonomous driving policies with strong closed-loop performance frequently lack timely internal predictions of surrounding vehicles during near-collision events, and causal correction of prediction errors leads to improved ego planning.
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DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
DeepSight uses parallel latent feature prediction in BEV for long-horizon world modeling and adaptive text reasoning to reach state-of-the-art closed-loop performance on the Bench2drive benchmark.