  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.
