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This paper proposes a structured reinforcement learning framework for Bayesian persuasion in interactive driving, where a lead vehicle selectively reveals traffic information to guide connected vehicles. The method introduces MAPL and SQP algorithms, achieving 30% cost efficiency over existing methods.