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CRII: SaTC: Cyber Resilient Localization and Navigation for Autonomous Vehicles

CRII：SaTC：自动驾驶汽车的网络弹性定位和导航

基本信息

批准号：
2104999
负责人：
MIZANUR RAHMAN
金额：
$ 17.5万
依托单位：
University of Alabama Tuscaloosa
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104999&HistoricalAwards=false
关键词：
CRII SaTC Cyber Resilient Localization

项目摘要

Autonomous vehicles (AVs) in all modes of transportation (be it surface, aviation, or maritime) require accurate and reliable localization and navigation services in order to perform their autonomous functions. In many cases, a Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) provides the required localization and navigation capabilities for surface transportation. A GPS (or GNSS) mostly depends on satellites and radio communication, which are subject to various obstructions, such as high-rise buildings, walls and ceilings in garages and tunnels, and even thick cloud cover. Besides these factors, GPS devices are also subject to intentional threats, such as radio interference, spoofing on communications, data manipulation on transmitted messages, the jamming of GPS receiver channels, and disruptions to the GPS infrastructures. Existing cyber-resilient solutions against GPS interference, such as high-definition maps and Wi-Fi or cellular-based technologies, are either data-intensive and costly or susceptible to signal interference and limited by coverage area. Alternatively, low-cost in-vehicle sensors (including gyroscopes, accelerometers, steering angle sensors, inertial measurement units, odometers, and cameras), which are not susceptible to signal interference, can provide effective strategies for detecting threats as well as locating and navigating AVs. To establish cyber-resilient localization and navigation for autonomous driving in a roadway environment, the goal of this research is to develop sensor fusion approaches that combine outputs from low-cost in-vehicle sensors with those of onboard geographical information systems. This proposed research addresses intentional and unintentional interference issues of GPS services in order to improve the operational safety of AVs. The project will first investigate and develop an approach to detect GPS interference (such as jamming, spoofing, and natural interference) by predicting and identifying vehicle states (including distance traveled, turning, and lane-change maneuvers) based on data from low-cost in-vehicle sensors and the onboard graphical information system. This initial research thrust adopts a three-step approach: (i) create different types of jamming and spoofing attacks using interference devices in both a laboratory and controlled real-world environment; (ii) generate attack and attack-free datasets in real-world scenarios for predicting and detecting vehicle state information; and (iii) develop a deep sensor fusion model and dynamic time warping algorithm to detect GPS interference using the generated attack and attack-free datasets. The second stage of the project will develop an integrated cyber-resilient navigation system using data from in-vehicle sensors and the onboard graphical information system to guide an AV towards its intended destination in a GPS-denied or GPS-compromised environment. This will involve a three-step process: (i) generate route creation and location information using the graphical information system; (ii) fuse steering angle and gyroscope data to identify lane changes and turning movements using deep fusion and dynamic time warping techniques developed in the first research thrust; and (iii) integrate graphical information and sensor-fusion data for localization and navigation when the GPS signal is compromised. The final stage of research will develop a proof-of-concept of the cyber-resilient localization and navigation system using a conventional vehicle with low-cost AV sensors. Broader impacts entail a transformation of autonomous vehicle operations under intentional and unintentional interference.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

所有运输方式（无论是地面、航空还是海上）中的自动驾驶汽车（AV）都需要准确可靠的定位和导航服务，以执行其自动驾驶功能。在许多情况下，全球定位系统（GPS）或全球导航卫星系统（GNSS）为地面运输提供所需的定位和导航能力。GPS（或GNSS）主要依赖于卫星和无线电通信，这些通信受到各种障碍物的影响，例如高层建筑，车库和隧道的墙壁和天花板，甚至厚云层。除了这些因素之外，GPS设备还受到故意的威胁，例如无线电干扰、通信欺骗、对所传输消息的数据操纵、GPS接收器信道的干扰以及对GPS基础设施的破坏。针对GPS干扰的现有网络弹性解决方案，如高清地图和Wi-Fi或基于蜂窝的技术，要么是数据密集型和昂贵的，要么容易受到信号干扰和覆盖区域的限制。或者，低成本的车载传感器（包括陀螺仪、加速度计、转向角传感器、惯性测量单元、里程计和摄像头）不易受信号干扰的影响，可以提供有效的策略来检测威胁以及定位和导航自动驾驶汽车。为了建立网络弹性定位和导航的自动驾驶在道路环境中，本研究的目标是开发传感器融合方法，结合联合收割机输出低成本的车载传感器与车载地理信息系统。这项拟议的研究解决了GPS服务的有意和无意干扰问题，以提高自动驾驶汽车的运行安全性。该项目将首先研究和开发一种方法，通过基于低成本车载传感器和车载图形信息系统的数据预测和识别车辆状态（包括行驶距离，转弯和变道机动）来检测GPS干扰（如干扰，欺骗和自然干扰）。这个初步的研究重点采用了三步方法：（i）在实验室和受控的真实世界环境中使用干扰设备创建不同类型的干扰和欺骗攻击;（ii）在真实世界场景中生成攻击和无攻击数据集，用于预测和检测车辆状态信息;以及（iii）开发深度传感器融合模型和动态时间规整算法，以使用生成的攻击和无攻击数据集来检测GPS干扰。该项目的第二阶段将开发一个集成的网络弹性导航系统，使用来自车载传感器和车载图形信息系统的数据，在GPS拒绝或GPS受损的环境中引导AV到达其预期目的地。这将涉及一个三步骤的过程：（i）使用图形信息系统生成路线创建和位置信息;（ii）融合转向角和陀螺仪数据，以使用在第一次研究中开发的深度融合和动态时间扭曲技术来识别车道变化和转弯运动;以及（iii）当GPS信号受损时，整合图形信息和传感器融合数据用于定位和导航。研究的最后阶段将使用具有低成本AV传感器的传统车辆开发网络弹性定位和导航系统的概念验证。更广泛的影响意味着在有意和无意干扰下自动驾驶汽车的操作发生转变。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。