Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning

TL;DR

This paper proposes a reinforcement learning-based algorithm to optimize HD map update services in autonomous driving, significantly reducing latency and improving data transmission over vehicular networks.

Contribution

It introduces a coverage-aware Q-learning algorithm to enhance HD map update quality of service in dynamic vehicular environments, addressing network congestion issues.

Findings

Latency improved by 75% compared to IEEE802.11p without QoS

Latency reduced by 73% with IEEE802.11 with QoS

Latency decreased by 10% using IEEE802.11p with new access category

Abstract

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.