CPS: Breakthrough: Selective Listening - Control for Connected Autonomous Vehicles in Data-Rich Environments

目前的两个趋势有望彻底改变未来汽车交通的安全性、可靠性和能效：(I)车辆之间、智能基础设施和其他移动设备之间的无线连接，以及(Ii)从驾驶员辅助到完全自动驾驶的自动驾驶。互联自动驾驶汽车(CAV)是一种网络物理系统，它使用越来越复杂的软件算法来控制在不确定的现实世界环境中移动的物理车辆。计划中的连通性法规和领先制造商在车辆自主性方面的最新进展表明，在不久的将来，CAV将无处不在。道路将是一个数据丰富的环境，大量连接到车辆、基础设施、个人电子产品和可穿戴设备的无线设备将传输多模式数据。在这种情况下，CAV面临两个严重的挑战：(1)机载计算限制可能意味着自主算法可以容纳的数据源数量的事实上的上限，并可能引入适当选择较小可用数据子集的问题，以及(2)由于无线电频率频谱的有限数量以及无线应用和最终用户数量的快速增长，频谱稀缺出现，而目前的通信协议无法满足这一要求。我们观察到这两个挑战实际上是相互关联的，同时解决它们是有益的。为此，本项目的目标是研究网络物理系统中自主技术和无线连接技术之间的双向交互作用。以计算机物理系统中的CAVS为例，我们建议研究在频谱稀缺、数据丰富的环境中，估计和控制算法如何影响软件定义的无线电通信以及受其影响。该项目的技术前提是强调数据丰富的环境，在这种环境中，太多的数据可能会淹没自主算法，例如车辆的实时短视距轨迹规划器。与文献中的规划算法相比，该方法是一种新的选择方法，它是随着轨迹规划而演变的动态选择。这种选择性连通性还通过限制空间区域来扫描潜在的连接来帮助无线频谱感知算法更快地收敛。提出的轨迹规划算法基于所谓的提升图法，它有望弥合快速几何路径规划算法和包含车辆动力学约束的较慢控制理论技术之间的差距。除了CAV之外，所提出的技术方法还可以应用于其他网络物理系统，其中多个非合作的代理通过无线信道进行通信。所提出的轨迹规划方法是足够通用的，以允许不同的具体应用的规划问题的公式和解决方案。

Two current trends promise to revolutionize the safety, reliability, and energy-efficiency of futureautomotive transportation: (i) wireless connectivity of vehicles to each other, to smart infrastructure,and to other mobile devices, and (ii) autonomy, ranging from driver assistance to full self-drivingautonomy. Connected autonomous vehicles (CAVs) are cyber-physical systems with increasingly complexsoftware algorithms in control of a physical vehicle moving in uncertain real-world environments.Planned connectivity regulations and recent advances in vehicular autonomy by leadingmanufacturers imply that CAVs will be ubiquitous in the near future. Roads will be data-richenvironments where a large number of wireless devices attached to vehicles, infrastructure, personalelectronics, and wearable gadgets will transmit multimodal data. In this scenario, two serious challenges arise for CAVs: (1) Onboard computational limitations may imply a de facto upper bound on the numberof data sources that can be accommodated by the autonomy algorithms, and may introduce the problem ofappropriately choosing a smaller subset of the available data, and (2) Given the finite amount of radio frequencyspectrum and the rapidly growing number of wireless applications and end-users, spectrum scarcity arises,for which current communication protocols do not suffice. We observe that these two challenges are in factintricately related, and that it is beneficial to address them together.To this end, the goal of this project is to investigate bidirectional interactions between thetechnologies of autonomy and of wireless connectivity in cyber-physical systems. Using CAVs asa case study in cyber-physical systems, we propose to investigate how estimation and controlalgorithms affect - and are affected by - software-defined radio communications in spectrum-scarce,data-rich environments. The technical premise of this project is an emphasis on data-rich environments,where too much data can overwhelm autonomy algorithms, e.g. real-time short-horizon trajectoryplanners for vehicles. The proposed approach of selecting the data sources that are likely to bethe "most informative" is a new aspect compared to planning algorithms in the literature.Furthermore, this selection is dynamic, in that it evolves with the trajectory plan. This selectiveconnectivity also helps the wireless spectrum sensing algorithm to converge faster by limiting thespatial regions to sweep for potential connections. The proposed trajectory planning algorithm isbased on the so-called method of lifted graphs, which promises to bridge the gap between fastgeometric path planning algorithms and slower control-theoretic techniques that incorporate vehicledynamical constraints. Beyond CAVs, the proposed technical approach can be applied to other cyber-physicalsystems where several non-cooperative agents communicate over wireless channels. The proposed trajectoryplanning approach is sufficiently general to allow the formulations and solutions of differentapplication-specific planning problems.