Literature Review of PNT and GNSS Threats and Vulnerabilities to HATS

The study team will conduct a thorough literature survey on topics relevant to assured PNT for automated vehicles. The literature review will gather and systematize existing knowledge related to the system components of PNT and HAV systems. In particular, it will treat opportunities, threats, and vulnerabilities related to (1) inertially-coupled GNSS receivers, (2) non-GNSS radionavigation via signals of opportunity (SOPs) and dedicated terrestrial beacons, (3) radar, lidar, and vision systems, (4) communications between vehicles and data/computation stored in cloud and edge servers, and (5) cooperative sensing: communication between vehicles and other traffic participants and infrastructure. The team's review will include USDOT guidance on PNT and existing information on GNSS vulnerabilities and integrity from publicly-available work from DoD, particularly as it relates to antenna systems and anti-jam techniques. Besides summarizing the state-of-the-art in these various categories, the team's review will identify gaps in the current knowledge and practice. (Task 1.1) Inertially Coupled GNSS Receivers Humphries The study team will begin by summarizing a series of prior survey articles and book chapters that they have identified as the state of the art in recent years, including the definitive book chapter on GNSS interference written by Humphreys, and the definitive book chapter on GNSS spoofing and detection co-authored by Humphreys. The study team will categorize and systematize the evaluation of top strategies for GNSS authentication and GNSS resilience by extending the approach that Humphreys. The study team will extend this work to include GNSS spoofing defenses and attacks introduced since 2016, such as the flexible multi-antenna meaconing attack and NovAtel’s layered defense, as well as GNSS denial of service (jamming). The study team will identify schemes for GNSS signal authentication and resilience that are well suited to implementation on highly automated vehicles (HAVs), such as those that exploit inertial sensing, multiple antennas, and signal quality monitoring. (Task 1.2) Non-GNSS Radionavigation Kassas The study team will identify terrestrial and LEO satellite signals of opportunity (SOPs) that could provide PNT for ground and aerial HAVs, with a focus on SOP-derived PNT integrity, availability, accuracy, and security. The team will study PNT security, continuity, and resiliency of dedicated terrestrial beacons (e.g., NextNav), which could be well suited to play a role as a GNSS backup for future HAVs. (Task 1.3) Radar, Lidar, and Vision Systems Chen HAVs, whether ground, aerial, or maritime, depend crucially on radar, lidar, and vision systems for collision avoidance and PNT. The use and performance of these technologies have been extensively covered in the literature, but their PNT impacts have seen little scrutiny. The study team will review the radar, lidar, and vision literature, points out gaps in current knowledge, and develops studies within the CARMEN UTC to address these gaps. (Task 1.4) Vehicle to Cloud/Edge Communication Ahmed High-definition (HD) maps stored in cloud or edge servers are a key asset for HATS: they enable vehicles to “expect the expected” in route planning and perception. These maps are essential for PNT because local sensing data are compared against them for localization. The study team will assess the vulnerability of vehicles to disruption or manipulation of vehicle-to-cloud/edge communication. (Task 1.5) Cooperative Sensing/Communication Kelly Cooperative sensing is a paradigm in which multiple vehicles and infrastructure exchange sensor data in real-time to amplify each vehicle’s situational awareness. Low-rate V2X protocols, such as DSRC, are capable of exchanging fully-digested situational estimates, such as vehicle poses, velocities, and hazard alerts. But to enable fuller situational awareness, a broader data sharing regime is necessary, one that exchanges raw sensor data such as images, radar and lidar returns, and GNSS observables. The study team will assess cooperative sensing opportunities and risks, including cooperatively derived PNT.