  Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive
information update services to ensure the safety of people and assets, and
satisfactory entertainment applications. Therefore, the freshness of
information is a crucial performance metric for CAV services. However,
information from roadside sensors and nearby vehicles can get delayed in
transmission due to the high mobility of vehicles. Our research shows that a
CAV's relative distance and speed play an essential role in determining the
Age-of-Information (AoI). With an increase in AoI, incremental service
aggregation issues are observed with out-of-sequence information updates, which
hampers the performance of low-latency applications in CAVs. In this paper, we
propose a novel AoI-based service aggregation method for CAVs, which can
process the information updates according to their update cycles. First, the
AoI for sensors and vehicles is modeled, and a predictive AoI system is
designed. Then, to reduce the overall service aggregation time and
computational load, intervals are used for periodic AoI prediction, and
information sources are clustered based on the AoI value. Finally, the system
aggregates services for CAV applications using the predicted AoI. We evaluate
the system performance based on data sequencing success rate (DSSR) and overall
system latency. Lastly, we compare the performance of our proposed system with
three other state-of-the-art methods. The evaluation and comparison results
show that our proposed predictive AoI-based service aggregation system
maintains satisfactory latency and DSSR for CAV applications and outperforms
other existing methods.
