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.

高清地图技术在自动驾驶中扮演关键角色，本论文提出了一种 Q-learning 算法来优化车载网络和高清地图更新，以解决网络拥塞和服务质量问题，实验结果显示，相较于其他方案，该算法显著提高了高清地图数据的延迟性能。

基于覆盖感知和强化学习的高清地图更新服务提升

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

Public transportation has been essential in people's lives in recent years.
Bus ridership is a factor in people's choice to board the bus. Therefore, from
the perspective of improving service quality, it is important to inform
passengers who have not boarded the bus yet about future bus ridership.
However, there is a concern that providing inaccurate information may cause a
negative experience. Against this backdrop, there is a need to provide bus
passengers who have not boarded yet with highly accurate predictions. Many
researchers are working on studies on this. However, two issues summarize
related studies. The first is that the correlation of bus ridership between
consecutive bus stops should be considered for the prediction. The second is
that the prediction has yet to be made using all of the features shown to be
useful in each related study. This study proposes a prediction method that
addresses both of these issues. We solve the first issue by designing an
LSTM-based architecture for each bus stop and a single model for the entire bus
stop. We solve the second issue by inputting all useful data, the past bus
ridership, day of the week, time section, weather, and precipitation, as
features. Bus ridership at each bus stop collected from buses operated by
Minato Kanko Bus Inc, in Kobe city, Hyogo, Japan, from October 1, 2021, to
September 30, 2022, were used to compare accuracy. The proposed method improved
RMSE by 23% on average and up to 27% compared to existing methods.

通过设计 LSTM-based 架构和输入过去的巴士乘客、星期几、时间段、天气情况和降雨量等所有相关数据作为特征，本研究提出了一种巴士乘客高度准确的预测模型，以提高服务质量。结果表明，该模型平均和最高准确率分别提高了 23% 和 27%。

考虑时间区间、天气和乘客数量趋势的多重 LSTM 公交车乘客量预测

Bus Ridership Prediction with Time Section, Weather, and Ridership Trend  Aware Multiple LSTM

The next-generation wireless networks are required to satisfy a variety of
services and criteria concurrently. To address upcoming strict criteria, a new
open radio access network (O-RAN) with distinguishing features such as flexible
design, disaggregated virtual and programmable components, and intelligent
closed-loop control was developed. O-RAN slicing is being investigated as a
critical strategy for ensuring network quality of service (QoS) in the face of
changing circumstances. However, distinct network slices must be dynamically
controlled to avoid service level agreement (SLA) variation caused by rapid
changes in the environment. Therefore, this paper introduces a novel framework
able to manage the network slices through provisioned resources intelligently.
Due to diverse heterogeneous environments, intelligent machine learning
approaches require sufficient exploration to handle the harshest situations in
a wireless network and accelerate convergence. To solve this problem, a new
solution is proposed based on evolutionary-based deep reinforcement learning
(EDRL) to accelerate and optimize the slice management learning process in the
radio access network's (RAN) intelligent controller (RIC) modules. To this end,
the O-RAN slicing is represented as a Markov decision process (MDP) which is
then solved optimally for resource allocation to meet service demand using the
EDRL approach. In terms of reaching service demands, simulation results show
that the proposed approach outperforms the DRL baseline by 62.2%.

本文介绍了一种新的管理网络切片的框架，该框架使用了基于演化的深度强化学习 (EDRL) 方法，将 O-RAN 切片表示为马尔可夫决策过程 (MDP)，并通过对资源分配的最优解来满足服务需求，模拟结果表明，所提出的方法比 DRL 基线提高了 62.2% 的服务质量。