Connected vehicular network is one of the key enablers for next generation
cloud/fog-supported autonomous driving vehicles. Most connected vehicular
applications require frequent status updates and Age of Information (AoI) is a
more relevant metric to evaluate the performance of wireless links between
vehicles and cloud/fog servers. This paper introduces a novel proactive and
data-driven approach to optimize the driving route with a main objective of
guaranteeing the confidence of AoI. In particular, we report a study on three
month measurements of a multi-vehicle campus shuttle system connected to
cloud/fog servers via a commercial LTE network. We establish empirical models
for AoI in connected vehicles and investigate the impact of major factors on
the performance of AoI. We also propose a Deep Q-Learning Netwrok (DQN)-based
algorithm to decide the optimal driving route for each connected vehicle with
maximized confidence level. Numerical results show that the proposed approach
can lead to a significant improvement on the AoI confidence for various types
of services supported.

本文介绍了一种利用数据驱动的方案，优化联网车辆的驾驶路线，以保证信息时延的可信度，利用商用 LTE 网络测量车辆之间的连接延迟，建立连接车辆信息时延模型，在此基础上提出了一种基于 DQN 的算法来确定最优驾驶路线，结果表明该方法可以提高接口保持服务的 AoI 信心水平