PRISM regularizes intermediate planning latents with a CVAE-style ELBO objective using ground-truth future paths, claiming an 8% L2 planning error reduction over deterministic baselines on nuScenes.
Autonomous driving in urban environments: Boss and the urban challenge,
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PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning
PRISM regularizes intermediate planning latents with a CVAE-style ELBO objective using ground-truth future paths, claiming an 8% L2 planning error reduction over deterministic baselines on nuScenes.