To address the" Market for Lemons" challenge in automotive data trading, we propose a blockchainbased market solution with a quality-aware Q-learning pricing algorithm. Our solution leverages consortium blockchain to create a transparent trading environment, using smart contracts to automate transactions and quality evaluations. The pricing mechanism innovatively incorporates data quality into reinforcement learning, enabling dynamic price adjustments based on market conditions and quality feedback. Through agent-based modeling simulations, we demonstrate that our approach effectively improves data quality while maintaining market fairness. This research contributes to both theory and practice of quality-driven pricing in blockchain-based data markets, particularly in the automotive industry where quality and trust are critical, providing valuable insights for similar systems in other data-intensive industries.
