Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data
processing and content sharing among vehicles and terrestrial edge computing
(TEC) infrastructures (e.g., 5G base stations and roadside units) with little
or no human intervention, plays a key role in the intelligent transportation
systems. However, EC-IoV is heavily dependent on the connections and
interactions between vehicles and TEC infrastructures, thus will break down in
some remote areas where TEC infrastructures are unavailable (e.g., desert,
isolated islands and disaster-stricken areas). Driven by the ubiquitous
connections and global-area coverage, space-air-ground integrated networks
(SAGINs) efficiently support seamless coverage and efficient resource
management, represent the next frontier for edge computing. In light of this,
we first review the state-of-the-art edge computing research for SAGINs in this
article. After discussing several existing orbital and aerial edge computing
architectures, we propose a framework of edge computing-enabled
space-air-ground integrated networks (EC-SAGINs) to support various IoV
services for the vehicles in remote areas. The main objective of the framework
is to minimize the task completion time and satellite resource usage. To this
end, a pre-classification scheme is presented to reduce the size of action
space, and a deep imitation learning (DIL) driven offloading and caching
algorithm is proposed to achieve real-time decision making. Simulation results
show the effectiveness of our proposed scheme. At last, we also discuss some
technology challenges and future directions.

本文从现有的轨道和空中边缘计算架构出发，提出了一种边缘计算增强的空中 - 地面 - 空间一体化网络（EC-SAGINs），以缩小任务完成时间和卫星资源使用。我们提出了一种预分类方案，以减小动作空间的大小，并提出了一种驱动卸载和缓存算法的深度模仿学习（DIL），以实现实时决策的目标。仿真结果表明我们的方案是有效的，并探讨了一些技术挑战和未来方向。

EC-SAGINs: 边缘计算增强的空地一体化车联网

EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks  for Internet of Vehicles

We consider the problem of predicting semantic segmentation of future frames
in a video. Given several observed frames in a video, our goal is to predict
the semantic segmentation map of future frames that are not yet observed. A
reliable solution to this problem is useful in many applications that require
real-time decision making, such as autonomous driving. We propose a novel model
that uses convolutional LSTM (ConvLSTM) to encode the spatiotemporal
information of observed frames for future prediction. We also extend our model
to use bidirectional ConvLSTM to capture temporal information in both
directions. Our proposed approach outperforms other state-of-the-art methods on
the benchmark dataset.

本文提出了一种新的模型，使用 ConvLSTM 编码观察到的视频帧的时空信息来预测未观察到的未来帧的语义分割图，并扩展了双向 ConvLSTM 来捕获双向的时间信息，该方法在基准数据集上优于其他最先进的方法，为实时决策制定提供了可靠的解决方案。

基于卷积 LSTM 的未来语义分割

Future Semantic Segmentation with Convolutional LSTM

Machine learning applications are increasingly deployed not only to serve
predictions using static models, but also as tightly-integrated components of
feedback loops involving dynamic, real-time decision making. These applications
pose a new set of requirements, none of which are difficult to achieve in
isolation, but the combination of which creates a challenge for existing
distributed execution frameworks: computation with millisecond latency at high
throughput, adaptive construction of arbitrary task graphs, and execution of
heterogeneous kernels over diverse sets of resources. We assert that a new
distributed execution framework is needed for such ML applications and propose
a candidate approach with a proof-of-concept architecture that achieves a 63x
performance improvement over a state-of-the-art execution framework for a
representative application.

针对机器学习在实时决策中的应用，提出需要一种新的分布式执行框架来实现毫秒延迟、高吞吐、任务图的自适应构建以及异构内核的执行等一系列要求，并提出了一个候选方案，其架构已经获得了 63 倍的性能提升。