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SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving
World models (WMs) have demonstrated strong potential for end-to-end autonomous driving by learning predictive representations of future scene dynamics. However, generating future videos during inference introduces substantial computational overhead, leading many recent driving WMs to adopt a single
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