Enhancement of High-definition Map Update Service Through Coverage-aware
  and Reinforcement Learning
Jeffrey Redondo Zhenhui Yuan Nauman Aslam
High-definition (HD) Map systems will play a pivotal role in advancing
autonomous driving to a higher level, thanks to the significant improvement
over traditional two-dimensional (2D) maps. Creating an HD Map requires a huge
amount of on-road and off-road data. Typically, these raw datasets are
collected and uploaded to cloud-based HD map service providers through
vehicular networks. Nevertheless, there are challenges in transmitting the raw
data over vehicular wireless channels due to the dynamic topology. As the
number of vehicles increases, there is a detrimental impact on service quality,
which acts as a barrier to a real-time HD Map system for collaborative driving
in Autonomous Vehicles (AV). In this paper, to overcome network congestion, a
Q-learning coverage-time-awareness algorithm is presented to optimize the
quality of service for vehicular networks and HD map updates. The algorithm is
evaluated in an environment that imitates a dynamic scenario where vehicles
enter and leave. Results showed an improvement in latency for HD map data of
$75\%$, $73\%$, and $10\%$ compared with IEEE802.11p without Quality of Service
(QoS), IEEE802.11 with QoS, and IEEE802.11p with new access category (AC) for
HD map, respectively.