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Optimizing autonomous driving policies directly against benchmark scores produces gains that can completely reverse under altered execution interfaces, revealing that top-performing models often exploit measurement artifacts rather than improving vehicle control.
Autonomous vehicles can now see better in the dark and rain thanks to a lightweight module that fuses per-frame perception with a learned global map, boosting geometric understanding and localization.
