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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.
CARVE-Q introduces a quantum-AI search layer for certified interactive driving repair, using quantum minimum finding on repair lattices while keeping safety authority classical. It provides structured certificates for vetoed maneuvers, achieving 100% right-of-way respect and blame consistency on INTERACTION replay scenarios.
CARVE is a certification framework for autonomous driving that provides runtime proofs for multi-agent repairs, accepting 98.64% of initially vetoed maneuvers on INTERACTION replay episodes without requiring predictions of other drivers' compliance.