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World-action models have recently improved autonomous driving by jointly learning future scene prediction and trajectory generation. Most existing approaches model the future primarily through RGB appearance, and recent works have begun to incorporate geometric prediction to improve spatial understanding. However, dens...
Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implausible actions. We propose the Large Discrete Policy (LDiP), a fully discrete behavior modeling framework that selects actions from a large vo...
This work introduces CASCADE (Causal Spatio-Temporal Analysis of Driving Environments), a structured scene representation for reasoning in driving scenes and a human-annotated dataset built on it that provides the reference for benchmarking the reasoning abilities of Physical AI models, and verifying the quality of aut...
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