The metaverse is a virtual space that combines physical and digital elements,
creating immersive and connected digital worlds. For autonomous mobility, it
enables new possibilities with edge computing and digital twins (DTs) that
offer virtual prototyping, prediction, and more. DTs can be created with 3D
scene reconstruction methods that capture the real world's geometry,
appearance, and dynamics. However, sending data for real-time DT updates in the
metaverse, such as camera images and videos from connected autonomous vehicles
(CAVs) to edge servers, can increase network congestion, costs, and latency,
affecting metaverse services. Herein, a new method is proposed based on
distributed radiance fields (RFs), multi-access edge computing (MEC) network
for video compression and metaverse DT updates. RF-based encoder and decoder
are used to create and restore representations of camera images. The method is
evaluated on a dataset of camera images from the CARLA simulator. Data savings
of up to 80% were achieved for H.264 I-frame - P-frame pairs by using RFs
instead of I-frames, while maintaining high peak signal-to-noise ratio (PSNR)
and structural similarity index measure (SSIM) qualitative metrics for the
reconstructed images. Possible uses and challenges for the metaverse and
autonomous mobility are also discussed.

提出了一种基于分布式辐射场（RFs）和多接入边缘计算（MEC）网络的新方法，用于视频压缩和元宇宙数字孪生（DT）更新，通过使用 RFs 代替 I 帧，可实现高达 80% 的数据节省，同时保持了重建图像的高峰信噪比（PSNR）和结构相似性指标（SSIM）质量测量。讨论了元宇宙和自主移动性的可能应用和挑战。

自动驾驶中分布辐射场用于边缘视频压缩和元宇宙集成

Distributed Radiance Fields for Edge Video Compression and Metaverse  Integration in Autonomous Driving

Autonomous mobility is emerging as a new mode of urban transportation for
moving cargo and passengers. However, such fleet coordination schemes face
significant challenges in scaling to accommodate fast-growing fleet sizes that
vary in their operational range, capacity, and communication capabilities. We
introduce the concept of partially observable advanced air mobility games to
coordinate a fleet of aerial vehicle agents accounting for their heterogeneity
and self-interest inherent to commercial mobility fleets. We propose a novel
heterogeneous graph attention-based encoder-decoder (HetGAT Enc-Dec) neural
network to construct a generalizable stochastic policy stemming from the inter-
and intra-agent relations within the mobility system. We train our policy by
leveraging deep multi-agent reinforcement learning, allowing decentralized
decision-making for the agents using their local observations. Through
extensive experimentation, we show that the fleets operating under the HetGAT
Enc-Dec policy outperform other state-of-the-art graph neural network-based
policies by achieving the highest fleet reward and fulfillment ratios in an
on-demand mobility network.

介绍了部分可观察的先进空中移动游戏的概念，提出了一种新的异构图注意力编码器 - 解码器 (HetGAT Enc-Dec) 神经网络，以构建可推广的随机策略来协调机动车辆代理，此政策通过深度一致性学习进行训练，使代理人能够使用其本地观察进行分散的决策制定。通过大量实验表明，采用 HetGAT Enc-Dec 政策的车队在按需移动网络中表现最佳。

高级空中机动的图形关注多智能体车队自治

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility

Many scenarios in mobility and traffic involve multiple different agents that
need to cooperate to find a joint solution. Recent advances in behavioral
planning use Reinforcement Learning to find effective and performant behavior
strategies. However, as autonomous vehicles and vehicle-to-X communications
become more mature, solutions that only utilize single, independent agents
leave potential performance gains on the road. Multi-Agent Reinforcement
Learning (MARL) is a research field that aims to find optimal solutions for
multiple agents that interact with each other. This work aims to give an
overview of the field to researchers in autonomous mobility. We first explain
MARL and introduce important concepts. Then, we discuss the central paradigms
that underlie MARL algorithms, and give an overview of state-of-the-art methods
and ideas in each paradigm. With this background, we survey applications of
MARL in autonomous mobility scenarios and give an overview of existing
scenarios and implementations.

该论文介绍了多智能体强化学习在自主移动中的应用，包括行为规划、车辆之间通信、性能提升等方面，并探讨了该领域的一些最新方法和想法。