An autoregressive diffusion driving model predicts each action before its corresponding future frame, using cross-modal denoising to beat prior planners on NAVSIM.
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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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ForgeDrive: Bidirectional Cross-Conditioning for Unified Visual-Action Generation in Autonomous Driving
An autoregressive diffusion driving model predicts each action before its corresponding future frame, using cross-modal denoising to beat prior planners on NAVSIM.
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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.