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Few-shot behavior cloning for autonomous driving achieves robust traffic light compliance by adaptively weighting demonstrations based on action density.
A higher-capacity policy trained on synthetic data can outperform simpler models, achieving human-level lane-following accuracy in real-world autonomous driving tasks.