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University of Wisconsin鈥揗adison
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EMAGN reduces the complexity of traffic forecasting models from quadratic to linear, enabling larger configurations without sacrificing performance.
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.
