CPS: Synergy: Collaborative Research: Harnessing the Automotive Infoverse

CPS：协同：协作研究：利用汽车信息宇宙

• 批准号: 1330118
• 负责人: Ramesh Govindan
• 金额: $ 46.19万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1330118&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Harnessing

Until now, the cyber component of automobiles has consisted of control algorithms and associated software for vehicular subsystems designed to achieve one or more performance, efficiency, reliability, comfort, or safety goals, primarily based on short-term intrinsic vehicle sensor data. However, there exist many extrinsic factors that can affect the degree to which these goals can be achieved. These factors can be determined from: longer-term traces of in-built sensor data that can be abstracted as triplines, socialized versions of these that are shared amongst vehicle users, and online databases. These three sources of information collectively constitute the automotive infoverse.This project harnesses this automotive infoverse to achieve these goals through high-confidence vehicle tuning and driver feedback decisions. Specifically, the project develops software called Headlight that permits the rapid development of apps that use the infoverse to achieve one or more goals. Advisory apps can provide feedback to the driver in order to ensure better fuel efficiency, while auto-tuning goals can set car parameters to promote safety. Allowing vehicles and such apps to share vehicle data with others and to use extrinsic information results in novel information processing, assurance, and privacy challenges. The project develops methods, algorithms and models to address these challenges.Broader Impact - This project can have significant societal impact by reducing carbon emissions and improving vehicular safety, can spur innovation in tuning methods and encourage researchers to experiment with this class of cyber-physical systems. The active participation of General Motors will strongly facilitate technology transfer. The program has outreach through internships, course material, high school and undergraduate involvement, and through creating an open infrastructure usable by diverse developers.

到目前为止，汽车的网络组件主要由车辆子系统的控制算法和相关软件组成，旨在实现一个或多个性能、效率、可靠性、舒适性或安全性目标，主要基于车辆传感器的短期内在数据。然而，存在许多外部因素会影响这些目标的实现程度。这些因素可以从以下几个方面来确定：可以抽象为三线的内置传感器数据的长期轨迹，在车辆用户之间共享的这些数据的社会化版本，以及在线数据库。这三个信息来源共同构成了汽车逆信息。该项目利用这种汽车逆操作，通过高可信度的车辆调整和驾驶员反馈决策来实现这些目标。具体来说，该项目开发了一款名为Headlight的软件，该软件允许快速开发应用程序，使用反转来实现一个或多个目标。咨询应用程序可以向驾驶员提供反馈，以确保更好的燃油效率，而自动调整目标可以设置汽车参数，以提高安全性。允许车辆和此类应用与他人共享车辆数据并使用外部信息会导致新的信息处理、保证和隐私挑战。该项目开发方法、算法和模型来应对这些挑战。更广泛的影响——该项目可以通过减少碳排放和提高车辆安全性来产生重大的社会影响，可以刺激调谐方法的创新，并鼓励研究人员对这类网络物理系统进行实验。通用汽车公司的积极参与将有力地促进技术转让。该项目通过实习、课程材料、高中和本科生的参与，以及通过创建一个可供不同开发者使用的开放基础设施进行推广。