Avatars, as promising digital assistants in Vehicular Metaverses, can enable
drivers and passengers to immerse in 3D virtual spaces, serving as a practical
emerging example of Artificial Intelligence of Things (AIoT) in intelligent
vehicular environments. The immersive experience is achieved through seamless
human-avatar interaction, e.g., augmented reality navigation, which requires
intensive resources that are inefficient and impractical to process on
intelligent vehicles locally. Fortunately, offloading avatar tasks to RoadSide
Units (RSUs) or cloud servers for remote execution can effectively reduce
resource consumption. However, the high mobility of vehicles, the dynamic
workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making
avatar migration decisions. To address these challenges, in this paper, we
propose a dynamic migration framework for avatar tasks based on real-time
trajectory prediction and Multi-Agent Deep Reinforcement Learning (MADRL).
Specifically, we propose a model to predict the future trajectories of
intelligent vehicles based on their historical data, indicating the future
workloads of RSUs.Based on the expected workloads of RSUs, we formulate the
avatar task migration problem as a long-term mixed integer programming problem.
To tackle this problem efficiently, the problem is transformed into a Partially
Observable Markov Decision Process (POMDP) and solved by multiple DRL agents
with hybrid continuous and discrete actions in decentralized. Numerical results
demonstrate that our proposed algorithm can effectively reduce the latency of
executing avatar tasks by around 25% without prediction and 30% with prediction
and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse
(AeVeM).

本研究提出了一个基于实时轨迹预测和多智能体深度强化学习技术的动态迁移框架，以解决虚拟汽车元宇宙中的任务迁移问题。数值结果表明，我们提出的算法可以有效地减少大约 25％的任务执行延迟，并改善用户在 AIoT-enabled Vehicular Metaverse (AeVeM) 中的沉浸式体验。

AIoT - 可控交通虚拟城市中基于多智能体深度强化学习的动态角色迁移与轨迹预测

Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in  AIoT-enabled Vehicular Metaverses with Trajectory Prediction

Driving safety analysis has recently experienced unprecedented improvements
thanks to technological advances in precise positioning sensors, artificial
intelligence (AI)-based safety features, autonomous driving systems, connected
vehicles, high-throughput computing, and edge computing servers. Particularly,
deep learning (DL) methods empowered volume video processing to extract
safety-related features from massive videos captured by roadside units (RSU).
Safety metrics are commonly used measures to investigate crashes and
near-conflict events. However, these metrics provide limited insight into the
overall network-level traffic management. On the other hand, some safety
assessment efforts are devoted to processing crash reports and identifying
spatial and temporal patterns of crashes that correlate with road geometry,
traffic volume, and weather conditions. This approach relies merely on crash
reports and ignores the rich information of traffic videos that can help
identify the role of safety violations in crashes. To bridge these two
perspectives, we define a new set of network-level safety metrics (NSM) to
assess the overall safety profile of traffic flow by processing imagery taken
by RSU cameras. Our analysis suggests that NSMs show significant statistical
associations with crash rates. This approach is different than simply
generalizing the results of individual crash analyses, since all vehicles
contribute to calculating NSMs, not only the ones involved in crash incidents.
This perspective considers the traffic flow as a complex dynamic system where
actions of some nodes can propagate through the network and influence the crash
risk for other nodes. We also provide a comprehensive review of surrogate
safety metrics (SSM) in the Appendix A.

通过 RSU 拍摄的影像处理方式，构建了新型的网络层面上的安全度量（NSMs）方法。同时，该文研究确定了车辆节点在网络中的作用，并探讨了交通网络中节点行为对系统整体状态的影响。