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University of Wisconsin鈥揗adison
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Despite advances in vision-language models, they still struggle to reason about the complex dynamics of traffic crashes, highlighting a critical gap in safety-critical applications.
Autonomous vehicles can now leverage the rich semantic understanding of VLMs for safer driving without the computational overhead, thanks to a clever training strategy that distills VLM knowledge into a real-time RL policy.
LLMs can boost autonomous driving behavior classification accuracy to over 94% by fusing numerical time-series data with high-level semantic features.
Achieve near-VLM autonomous driving performance with a lightweight RL model running at 500 FPS by asynchronously distilling knowledge from VLMs.
