MVAdapt conditions end-to-end autonomous driving policies on explicit vehicle physics to achieve better zero-shot transfer and few-shot calibration across different vehicles in CARLA simulation.
Safety-enhanced autonomous driving using interpretable sensor fusion transformer
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VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher non-collision rate in closed-loop tests.
citing papers explorer
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MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving
MVAdapt conditions end-to-end autonomous driving policies on explicit vehicle physics to achieve better zero-shot transfer and few-shot calibration across different vehicles in CARLA simulation.
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VERDI: VLM-Embedded Reasoning for Autonomous Driving
VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher non-collision rate in closed-loop tests.