A Blockchain-based Model Sharing and Calculation Method for Urban Rail Intelligent Driving Systems

Abstract

In urban rail transit systems, the intelligent driving system is gradually replacing manual driving for its high safety, punctuality, and stopping accuracy. With the development of big data analytics, the data-driven intelligent driving system becomes a research focus. Traditional datadriven intelligent driving systems suffer from inadequate data. Due to lacking effective incentives and trust, data from different urban rail operators cannot be shared directly. In this paper, we propose a framework that uses blockchain technology to realize sharing and collaborative training of intelligent driving models between operators. In this framework, we use blockchain-based distributed federated reinforcement learning methods to complete intelligent driving calculations. We use smart contracts to implement the management of the entire federal reinforcement learning. Operators use local historical data to participate in intelligent driving training based on Q-network by exchanging encrypted model parameters, and optimize the safety distance, energy consumption, and punctuality of urban rail transit systems. Simulation results show that our proposed distributed federated reinforcement learning method can significantly improve the intelligent driving system performance.