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Miscommunication in mixed autonomy driving can be mitigated by a framework that prioritizes inquiry and listening, leading to a 20% reduction in lane change prediction errors.
Synthesizing realistic defect data with diffusion models and Perlin noise can dramatically improve unsupervised anomaly detection, achieving near-perfect AUROC scores on industrial surfaces.
