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

CPS：突破：选择性聆听 - 数据丰富环境中联网自动驾驶车辆的控制

• 批准号: 1646367
• 负责人: Raghvendra Cowlagi
• 金额: $ 42.51万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646367&HistoricalAwards=false
• 关键词: CPS; Breakthrough; Selective; Listening; Control

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.

目前有两个趋势有望彻底改变未来汽车运输的安全性、可靠性和能源效率：（i）车辆之间、与智能基础设施之间以及与其他移动的设备之间的无线连接;（ii）自动驾驶，从驾驶员辅助到完全自动驾驶。互联自动驾驶汽车（Connected Autonomous Vehicles，CAV）是一种网络物理系统，其软件算法越来越复杂，可以控制在不确定的现实环境中移动的实体车辆。道路将是数据丰富的环境，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面临两个严峻的挑战：（1）机载计算限制可能意味着自治算法可以容纳的数据源数量的实际上限，并且可能引入适当选择可用数据的较小子集的问题，以及（2）给定有限的无线电频谱量以及快速增长的无线应用和最终用户数量，出现了当前通信协议不能满足的频谱稀缺性。我们观察到这两个挑战实际上是紧密相关的，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治技术和无线连接技术之间的双向交互。使用CAV阿萨案例研究在网络物理系统中，我们建议调查如何估计和控制算法的影响-并受到影响-软件定义的无线电通信在频谱稀缺，数据丰富的环境。该项目的技术前提是强调数据丰富的环境，在这种环境中，过多的数据可能会压倒自主算法，例如车辆的实时短视野自主规划器。与文献中的规划算法相比，所提出的选择可能是“最具信息性”的数据源的方法是一个新的方面。此外，这种选择是动态的，因为它随着轨迹规划而发展。这种选择性连接还有助于无线频谱感知算法通过限制扫描潜在连接的空间区域来更快地收敛。所提出的轨迹规划算法是基于所谓的提升图的方法，它承诺弥合之间的差距差距快速几何路径规划算法和较慢的控制理论技术，包括车辆动力学约束。除了CAV之外，所提出的技术方法可以应用于其他网络物理系统，其中几个非合作代理通过无线信道进行通信。所提出的规划方法是足够普遍的，允许不同application-specific规划问题的配方和解决方案。

项目成果

Near-optimal task-driven sensor network configuration

近乎最优的任务驱动传感器网络配置

• DOI: 10.1016/j.automatica.2023.110966
• 发表时间: 2023
• 期刊: Automatica
• 影响因子: 6.4
• 作者: St. Laurent, Chase;Cowlagi, Raghvendra V.
• 通讯作者: Cowlagi, Raghvendra V.

Risk Quantification for Automated Driving using Information from V2V Basic Safety Messages

• DOI: 10.1109/vtc2021-spring51267.2021.9448849
• 发表时间: 2021-04
• 期刊: 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)
• 作者: Raghvendra V. Cowlagi;Rebecca C. Debski;A. Wyglinski

Depth-first coupled sensor configuration and path-planning in unknown static environments

未知静态环境中的深度优先耦合传感器配置和路径规划

• 发表时间: 2021
• 期刊: 2021 European Control Conference
• 作者: St. Laurent, C. L;Cowlagi, R. V.
• 通讯作者: Cowlagi, R. V.

Assessment of Positioning Errors on V2V Networks Employing Dual Beamforming

采用双波束成形的 V2V 网络定位误差评估

• DOI: 10.1109/vtcfall.2018.8690921
• 发表时间: 2018
• 期刊: 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall
• 作者: Kanthasamy, Nivetha;Du, Ruixiang;Gill, Kuldeep S.;Wyglinski, Alexander M.;Cowlagi, Raghvendra
• 通讯作者: Cowlagi, Raghvendra

State Estimation for Mitigating Positioning Errors in V2V Networks Employing Dual Beamforming

• DOI: 10.1109/vtcfall.2018.8690869
• 发表时间: 2018-08
• 期刊: 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)
• 作者: Nivetha Kanthasamy;Raghvendra V. Cowlagi;A. Wyglinski