The increasing interest in Autonomous Vehicles (AV) is notable due to
business, safety, and performance reasons. While there is salient success in
recent AV architectures, hinging on the advancements in AI models, there is a
growing number of fatal incidents that impedes full AVs from going mainstream.
This calls for the need to revisit the fundamentals of building safety-critical
AV architectures. However, this direction should not deter leveraging the power
of AI. To this end, we propose Savvy, a new trustworthy intelligent AV
architecture that achieves the best of both worlds. Savvy makes a clear
separation between the control plane and the data plane to guarantee the
safety-first principles. The former assume control to ensure safety using
design-time defined rules, while launching the latter for optimizing decisions
as much as possible within safety time-bounds. This is achieved through guided
Time-aware predictive quality degradation (TPQD): using dynamic ML models that
can be tuned to provide either richer or faster outputs based on the available
safety time bounds. For instance, Savvy allows to safely identify an elephant
as an obstacle (a mere object) the earliest possible, rather than optimally
recognizing it as an elephant when it is too late. This position paper presents
the Savvy's motivations and concept, whereas empirical evaluation is a work in
progress.

自动驾驶汽车的关键问题是安全性，本文提出了一种名为 Savvy 的可信智能自动驾驶汽车架构，通过控制层和数据层的明确分离来确保安全，并通过动态机器学习模型在安全时间限制内进行优化决策。

精明：可信任的自动驾驶车辆架构

Savvy: Trustworthy Autonomous Vehicles Architecture

Future wireless networks are expected to evolve towards an intelligent and
software reconfigurable paradigm enabling ubiquitous communications between
humans and mobile devices. They will be also capable of sensing, controlling,
and optimizing the wireless environment to fulfill the visions of low-power,
high-throughput, massively-connected, and low-latency communications. A key
conceptual enabler that is recently gaining increasing popularity is the
Holographic Multiple Input Multiple Output Surface (HMIMOS) that refers to a
low-cost transformative wireless planar structure comprising of sub-wavelength
metallic or dielectric scattering particles, which is capable of impacting
electromagnetic waves according to desired objectives. In this article, we
provide an overview of HMIMOS communications by introducing the available
hardware architectures for reconfigurable such metasurfaces and their main
characteristics, as well as highlighting the opportunities and key challenges
in designing HMIMOS-enabled communications.

本文介绍了未来无线网络的一种智能化、可重新配置的模式，及其关键概念 Holographic Multiple Input Multiple Output Surface（HMIMOS）的信息。该模式采用亚波长金属或介电散射粒子製作成的 HMIMOS 面板，可以实现低成本的无线通信，具有优化电磁波传输、低功耗、高吞吐量、低延迟等特点。本文综述了该裝置的特点、机会和挑战。