Intelligent Transportation System (ITS) has become one of the essential components in Industry 4.0. As one of the critical indicators of ITS, efficiency has attracted wide attention from researchers. However, the next generation of urban traffic carried by multiple transport service providers may prohibit the raw data interaction among multiple regions for privacy reasons, easily ignored in the existing research. This paper puts forward a federated learning-based vehicle control framework to solve the above problem, including interactors, trainers, and an aggregator. In addition, the density-aware model aggregation method is utilized in this framework to improve vehicle control. What is more, to promote the performance of the end-to-end learning algorithm in the safety aspect, this paper proposes an imitation learning algorithm, which can obtain collision avoidance capabilities from a set of collision avoidance rules. Furthermore, a loss-aware experience selection strategy is also explored, reducing the communication overhead between the interactors and the trainers via extra computing. Finally, the experiment results demonstrate that the proposed imitation learning algorithm obtains the ability to avoid collisions and reduces discomfort by 55.71%. Besides, density-aware model aggregation can further reduce discomfort by 41.37%, and the experience selection scheme can reduce the communication overhead by 12.80% while ensuring model convergence.
翻译:智能运输系统(ITS)已成为工业4.0.0.。 作为ITS的关键指标之一,效率吸引了研究人员的广泛关注。然而,由多个运输服务提供商携带的下一代城市交通由于隐私原因可能禁止多个区域之间的原始数据互动,而现有研究对此很容易忽视。本文件提出了一个基于学习的联邦车辆控制框架,以解决上述问题,包括互动者、培训员和聚合器。此外,在这个框架中使用了密度认知模型集成方法,以改进车辆控制。此外,为了在安全方面促进端到端学习算法的性能,本文建议采用模拟学习算法,这种算法可以从一套避免碰撞的规则中获得避免碰撞的能力。此外,还探讨了一种认识损失的经验选择战略,通过额外计算减少互动者与培训者之间的通信间接费用。最后,实验结果表明,拟议的模拟学习算法获得了避免碰撞和减少不兼容性的能力。此外,通过降低密度12.80模型集成率,同时通过选择磁性组合法,可以进一步降低磁性组合率。