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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Prototype Development and Pilot Deployment of Ground-Based Intelligent Infrastructure for Resilient Positioning, Navigation, and Timing</title>
      <link>https://rip.trb.org/View/2696990</link>
      <description><![CDATA[Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), form the backbone of modern positioning, navigation, and timing (PNT) services. However, these space-based systems are inherently vulnerable to cyberattacks such as jamming, spoofing, as well as unintentional interference, including signal blockage, particularly in dense urban areas, indoor environments, and adversarial environments. The growing dependence on GNSS, driven by the rapid adoption of autonomous and connected systems, has exposed a single point of failure in the global PNT infrastructure. GPS signals are extremely weak at the Earth’s surface, enabling low-cost jammers or spoofers to easily disrupt receivers. In response to the 2020 Executive Order on strengthening national resilience through responsible use of PNT services signed by President Donald J. Trump, US DOT, the Department of War (DoW), and the Department of Homeland Security (DHS) have jointly emphasized the need for complementary and backup PNT capabilities that are interoperable and independently capable of sustaining precision timing and navigation for critical infrastructure during GNSS outages or cyberattacks. The research goal is to develop and demonstrate a prototype ground-based, GPS-compatible, cyber-secure PNT architecture that can generate, synchronize, and broadcast authenticatable GPS-like signals from a network of ground-based nodes, allowing existing GPS receivers to obtain valid PNT solutions without hardware modification. This goal will be achieved through the following specific research objectives: (1) Design and generate authenticable GPS-compatible terrestrial signals that replicate the L1 C/A (coarse/acquisition) waveform while embedding virtual ephemeris and adjusted clock-offset parameters to enable accurate and PNT computation from ground transmitters. (2) Develop intelligent terrestrial nodes (at least four nodes) equipped with chip-scale atomic clocks, edge computer, and transmitters to establish a distributed ground-based PNT architecture. (3) Synchronize terrestrial nodes with a master clock using precision timing distribution techniques to maintain consistent and reliable time alignment across the network. Real-Time Kinematic (RTK) positioning and differential methods will also be explored using the GEODNET hub within the UA network. (4) Demonstrate that an off-the-shelf GPS receiver can deliver a valid PNT solution using terrestrial signals through software-only modifications, thereby validating the practicality, backward compatibility, and deployment readiness of the proposed system.
]]></description>
      <pubDate>Wed, 29 Apr 2026 16:45:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696990</guid>
    </item>
    <item>
      <title>Evaluating User Acceptance and Effectiveness of Cognitive Measurements and Intervention for Shared Autonomy</title>
      <link>https://rip.trb.org/View/2690985</link>
      <description><![CDATA[Vehicles equipped with automated driving systems (ADS) have become more widespread in the trucking industry. On the one hand, ADS are known to be susceptible to occasional errors in environment perception, but on the other, ADS can demonstrate safer and more efficient behavior in situations where the driver is cognitively impaired. Shared autonomy systems thus have the potential to combine the best of both paradigms. Some early instantiations of such shared autonomy ADS use measurements of the human cognitive state to perform interventions, either in the form of sensory feedback, and/or by actively taking over the driving task. The main objective of this project is to address the gap in research on the effectiveness and acceptance of cognition-aware shared-autonomy methods with respect to the overall system safety. Qualitative data will be collected through semi-structured interviews with truck drivers and systematically encoded into operational design requirements and hypothesis-driven performance metrics that directly inform the design of cognition-aware shared autonomy systems. The research team will perform a driving simulator study that enables a controlled evaluation of adaptive cognition-aware intervention policies, including rule-based and data-driven triggering mechanisms that dynamically adjust system behavior based on real-time cognitive interventions. Researchers will study how specific design choices in cognition-aware intervention policies (e.g., trigger thresholds, modality selection, and intervention persistence) influence system acceptance, misuse, and compliance, enabling actionable design guidance beyond descriptive acceptance analysis. The datasets collected inform policy on the use of ADS in both drayage and long-haul trucking. This project will develop a methodology for designing and evaluating cognition-aware behavioral interventions that couple driver monitoring outputs with explicit control and feedback policies, enabling reproducible comparison across intervention strategies and deployment contexts.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:23:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2690985</guid>
    </item>
    <item>
      <title>Ensuring PNT Resiience</title>
      <link>https://rip.trb.org/View/2676001</link>
      <description><![CDATA[With CARMEN+ support the research team has characterized the timing properties of modulation from the Starlink constellation in order to assess its suitability for providing opportunistic pseudorange-based positioning, navigation, and timing (PNT) as a backup to Global Navigation Satellite System (GNSS). With the same purpose, the team has also uncovered key features of the OneWeb signal structure and has demodulated its data for the first time. The findings have indicated that opportunistic pseudorange-based PNT is not feasible using Starlink signals without aiding from a network of ground receivers. But given such a network, the team has achieved 10-meter-level positioning and 30-ns timing using Starlink signals. The next phase will extend this project along several lines: (i) characterize the modulation timing stability of OneWeb as the team has done with Starlink, (ii) deploy a network of 2 or 3 reference stations so that all ephemeris and transmission time modeling errors may be eliminated, (iii) employ super-resolution techniques to more precisely estimate modulation (e.g., Starlink frame) time of arrival, and (iv) analyze the pattern of assigned beams and side beams from Starlink satellites to predict how many unique satellites would typically be available for a PNT solution, and with what dilution of precision. For these studies, the team will capture and analyze broadband Starlink, OneWeb, and Kuiper data with their own RF equipment from multiple stations. The team believes that the outcome of this work will be of great importance, namely, a backup PNT system with global reach, decimeter positioning, nanosecond timing, inherent signal authentication (via cross-checking unpredictable broadband payload data and against a reference network), and improved resistance to jamming compared to traditional GNSS. Furthermore, the team aims to transfer this technology to their project partners for commercialization.]]></description>
      <pubDate>Mon, 02 Mar 2026 19:15:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676001</guid>
    </item>
    <item>
      <title>Weather Radar Augmented Positioning (WRAP) Technology for Aerial Vehicles</title>
      <link>https://rip.trb.org/View/2675937</link>
      <description><![CDATA[Problem Statement: As the modern transportation
navigation systems increasingly rely on Global Positioning System/Global Navigation Satellite System (GPS/GNSS) signals, the potential transportation safety risks also
increase significantly should there be intruptions in
transmitting or receiving GPS/GNSS signals in the
events such as strong solar wind activities,
unintentional interferences, or intentional jamming.
To mitigate these transportation safety risks, several
alternative positioning technologies are being
actively developed. These technologies include using
ground-base GPS/GNSS pseudolites and using signal
of opportunities (SOP) transmitted from cellular
towers and LEO communication satellites. However,
the deployment of ground-based GPS/GNSS
pseudolites are still very limited and facing several regulation hurdles over potentially causing
interferences and human RF exposure risks. The coverage of cellular signals are still lacking in rural
or remote areas, and not available at higher altitude above the ground level (AGL), and using LEO
satellite signals for navigation are more complex and expensive. This project will explore the
utilization of the radar signals transmitted from the existing NEXRAD WSR-88D Weather Radar
Network for navigating aerial vehicles. These signals are within the designated 2700-3000 MHz
frequency band with 25 MHz bandwidth. The two key advantage of these SOP signals are strong
signal strength and wide coverage of almost entire US.
Objectives: The main research objectives of this project include demonstrating the feasibility of
using the strong ubiquatus weather radar signals for aerial vehicle navigation needs, defining the
key system hardware and software requirements, and identifying performance limitations.
Scope: This 12-month research effort will include (1) characterizing the NEXRAD signal strengths
and waveforms, (2) investigating distributed receiving antenna strategy, (3) developing positioning
algorithms, and (4) analyzing the positions accuracy and limitations.]]></description>
      <pubDate>Mon, 02 Mar 2026 15:37:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675937</guid>
    </item>
    <item>
      <title>Real-time Fleet Composition via Machine Vision and AI for use in Pedestrian Safety and Risk Exposure Studies
</title>
      <link>https://rip.trb.org/View/2669652</link>
      <description><![CDATA[In 2024, Georgia Tech researchers developed automated procedures to capture very consistent vehicle images using portable high-resolution video cameras positioned on Interstate overpasses.  The team collected and processed more than one-million vehicle images from four locations in the Atlanta Metro Area for a State Road and Tollway Administration research project, in which a large subset of vehicle images were coded by make-and-model.  The team subsequently developed machine-vision models and AI tools to identify vehicle make-and-model combinations as part of a 2024 CHEM research project, for use in a variety of pedestrian safety and risk exposure studies.  In the initial model development work, the team worked in Georgia Tech’s PACE distributed computing system.  However, the resulting models are so fast, the team has concluded that the AI fleet composition models can run in real-time.

In this follow-on project, the research team will refine the current models to further reduce computational requirements so that the AI models can be used in edge-computing, which will process vehicle fleet composition on site, without transmitting video data to a data center.  The team’s second challenge is to design and package an efficient portable computing system with a high-end graphics card that can operate under year-round temperature and humidity conditions.  The team will balance system performance with power- draw and heating/cooling requirements.  Overpass video is very consistent, providing elevated and unobscured rear views of vehicles.  In the third phase of the project, the team will develop protocols for collecting video from major arterials and will develop machine-vision models from a wider variety of camera views (as constrained by intersection design and safe placement of equipment).  The team anticipates that arterial corridor implementation will be much more complicated and that strict camera placement protocols may be needed to reach the accuracy of overpass-image-derived models.

The team anticipates that equipment development (downsizing, enclosure design, heat dissipation, power consideration, etc.) and machine vision model implementation may lead to patentable inventions or licensable software.  If successful equipment deployments are afforded patent protection, the team will work with Tech’s commercialization office (commercialization.gatech.edu) to develop license agreements for the manufacture of equipment and deployment of portable edge-computing systems and/or will create a GT Create-X business startup.  If the USPTO rejects the patent claims, the team will release equipment specifications, software code, and technology transfer reports under open-source licensing that will allow state DOTs and their consultants to implement the systems.
]]></description>
      <pubDate>Sun, 15 Feb 2026 16:27:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669652</guid>
    </item>
    <item>
      <title>Develop and Test Optimal Speed Control Strategies for Connected and Automated Vehicles under GPS Jamming and Spoofing</title>
      <link>https://rip.trb.org/View/2640714</link>
      <description><![CDATA[This research project engages in transformative innovation to develop and evaluate optimal speed control strategies for Connected and Automated Vehicles (CAVs) navigating signalized intersections under conditions of Global Positioning System (GPS) jamming and spoofing. While recent studies have primarily focused on robust detection and mitigation techniques to safeguard CAV navigation, this study represents a first attempt to investigate the impact of GPS jamming and spoofing on CAV speed control applications and develop solutions to address these cyber attacks. The algorithms developed in this study also will be beneficial for extending into other CAV applications such as routing and platooning strategies. Building upon the Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I) framework, the study will generate real-time, fuel-efficient trajectories and enhance traffic flow efficiency within designated control zones. To ensure resilience against cyberattacks targeting GNSS signals, the project will simulate GPS spoofing and jamming scenarios in MATLAB and subsequently scale the evaluation to network-level performance using the INTEGRATION platform. Detection methods will leverage signal anomalies, estimation residuals, and cooperative vehicle-to-everything (V2X) cross-checks to identify compromised positioning data. The study further introduces a novel mitigation strategy using Optical Intelligent Reflecting Surfaces (OIRS) to enable dual-channel communication via RF and visible light. These OIRS-enabled systems will deliver authenticated positioning and timing data from roadside units, allowing the Eco-CACC-I controller to gracefully degrade and reweight sensor inputs when GNSS integrity is compromised. The outcome will be a robust, simulation-ready control strategy that enhances safety, fuel efficiency, and cyber-resilience for future CAV deployments. 

OBJECTIVES: The primary objective of this research is to develop and validate optimal speed control strategies for CAVs operating under compromised positioning conditions caused by GPS jamming and spoofing. By advancing the Eco-CACC-I framework, the study will produce fuel-efficient speed profiles for multiple vehicle powertrains, addressing both deceleration and uninterrupted cruising scenarios. These strategies will be tested in MATLAB and scaled to network-level simulations in INTEGRATION, aligning with USDOT priorities to promote safety, reduce congestion, and improve mobility and infrastructure durability. This study directly supports the statutory mission of the CARNATIONS UTC to toughen, augment, and protect Positioning, Navigation, and Timing (PNT) systems for multimodal surface transportation. ]]></description>
      <pubDate>Tue, 16 Dec 2025 15:20:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640714</guid>
    </item>
    <item>
      <title>Safe and Personalized Control of Autonomous Vehicles with On-Board Vision Language
Models: System Design and Real-World Validation
</title>
      <link>https://rip.trb.org/View/2625313</link>
      <description><![CDATA[This project focuses on enhancing autonomous vehicle control systems by integrating on-board Vision-Language Models (VLMs) for safe and personalized driving experiences. Building on the previously awarded Center for Connected and Automated Transportation
(CCAT) project on “CAV Pilot Development and Deployment in Midwest Winter,” this research addresses critical challenges in autonomous vehicle development regarding limited on-board computational resources by implementing lightweight VLM frameworks and Retrieval-Augmented Generation (RAG)-based memory modules. The project will validate the system’s ability to handle challenging urban scenarios, reduce human takeover rates, and adapt to diverse environmental conditions.
]]></description>
      <pubDate>Thu, 13 Nov 2025 15:43:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625313</guid>
    </item>
    <item>
      <title>Real-time Surface Monitoring for Improved Safety, Response, And Repair</title>
      <link>https://rip.trb.org/View/2593995</link>
      <description><![CDATA[The recent, rapid, and overwhelming movement of the Hooskanaden landslide in 2019 and the Arizona Inn landslide in early 2023 greatly disrupted traffic along US 101 with several days of full road closure followed by prolonged reduced capacity and weeks of traffic control for repairs. Alarmingly, numerous precarious landslides exist throughout the state that can result in similar consequences, triggered by precipitation or erosion—both of which will be exacerbated with climate change. Real-time, on-site instrumentation is essential to characterize landslide kinematics as well as detect and predict movements that can disrupt the highway system. Real-time, on-site instrumentation can also help inform the timing of repair and estimation of material needs, improving both on-site safety and maintenance costs associated with repair. However, the standard methodology for landslide instrumentation requires costly drilling beneath the earth’s surface—which is frequently unsafe, costly, and infeasible on an active landslide. Further, drilled subsurface instrumentation is oftentimes destroyed with landslide movement, providing only short-term usefulness for obtaining active landslide data. This project will develop and deploy low-cost surface monitoring strategies to monitor landslide movements, leveraging Oregon Department of Transportation's (ODOT's) recent proof-of-concept success using real-time kinematic global navigation satellite systems (RTK-GNSS) to monitor the Arizona Inn landslide failure, which enabled real-time delivery of critical information to help inform closure actions and repairs. Further development and establishment of surface monitoring methods will also inform statewide characterization of active slides that impact ODOT infrastructure where drilling is cost prohibitive, unsafe, or impossible.]]></description>
      <pubDate>Thu, 28 Aug 2025 15:09:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593995</guid>
    </item>
    <item>
      <title>Safe and Reliable Autonomous Vehicle Navigation through Cyber Resilience</title>
      <link>https://rip.trb.org/View/2531083</link>
      <description><![CDATA[The reliable operation of Autonomous Vehicles (AVs) hinges on robust and reliable Positioning, Navigation, and Timing (PNT) services, predominantly provided by Global Navigation Satellite Systems (GNSS). The U.S.-owned Global Positioning System (GPS) consists of Ground Control Stations (GCS), Space Vehicles (SV), and user segment receivers, all of which could be susceptible to natural interferences and cyber threats. GCS, vulnerable to physical and cyberattacks, can transmit compromised correction data to satellites, posing significant risks to navigation integrity. GNSS signals are inherently weak and susceptible to unintentional interference, such as signal blocking, urban canyon multipath, and atmospheric effects, as well as deliberate threats like jamming and spoofing, which significantly amplify uncertainties in PNT services. Although alternative PNT solutions, including Low Earth Orbit (LEO) satellites, Wi-Fi, and cellular-based technologies, show promise, they remain limited in coverage, underdeveloped, and/or vulnerable to intentional interference. High-definition (HD) map-based navigation systems are also at risk of exploitation by hackers. Multi-sensor fusion systems, integrating GNSS with inertial measurement units (IMU) and perception sensors (PS), such as cameras, LiDAR, and RADAR, offer potential solutions by complementing individual sensor outputs in contested environments. However, IMUs suffer from error accumulation, and PS performance is compromised by limited line-of-sight or adverse weather conditions (e.g., snow and heavy rain), which degrade positioning accuracy. To overcome these challenges, the overarching goal of this project is to enhance the security of GNSS-based navigation systems through four key objectives: (1) identifying and analyzing vulnerabilities in GNSS ground control and user segments to develop intelligent cyber-attack models, (2) designing and implementing sensor fusion algorithms that leverage loosely coupled GNSS, IMU, and perception sensor data for the detection of GNSS cyber-attacks, (3) developing advanced mitigation strategies to counter spoofing attacks and restore authentic GNSS signal lock, and (4) deploying these detection and mitigation algorithms in secured execution environments (TEEs) to safeguard operational integrity against software-based threats. By addressing GNSS vulnerabilities, the research will significantly enhance the safety and reliability of GNSS-based navigation for autonomous vehicles, foster public and industry reliability in these technologies, and support broader advancements in transportation cybersecurity.]]></description>
      <pubDate>Mon, 31 Mar 2025 17:16:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2531083</guid>
    </item>
    <item>
      <title>Alaska Continuously Operating Reference Network (ACORN) GNSS</title>
      <link>https://rip.trb.org/View/2512629</link>
      <description><![CDATA[As Alaska is late to the implementation game, there is an opportunity to skip the physical infrastructure and base station installation and deploy NTRIP (Network Transport of RTCM via Internet Protocol). This would allow for the creation of a VRS (Virtual Reference Station) where needed allowing for field crews, drones or self-driving cars to stay connected as needed. This project allows the saving of a considerable amount of money by deploying a digital public service while laying the foundation for next gen precision technologies.]]></description>
      <pubDate>Fri, 21 Feb 2025 22:21:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2512629</guid>
    </item>
    <item>
      <title>PNT Protection through Interference Monitoring and Robust Backups to GNSS </title>
      <link>https://rip.trb.org/View/2458990</link>
      <description><![CDATA[Central to civil infrastructure (e.g., transportation, communications) is the problem of robust and secure position, navigation and timing (PNT). This is especially true of urban environments, which offer more challenges, such as interference and multipath, when compared to open-sky conditions. As the only positioning system that currently offers absolutely-referenced meter-level accuracy with global coverage, the Global Navigation Satellite System (GNSS) will no doubt play a significant role in civil infrastructure going forward. For example, if strengthened against jamming and spoofing, carrier-phase-differential GNSS (CDGNSS), coupled with low-cost inertial sensing, will be nearly sufficient for position, velocity, and timing (PVT) needs of urban air mobility (UAM). But nearly sufficient is insufficient: it is not enough for a UAM PVT solution to offer decimeter-accurate positioning with 99% availability, or even 99.9% availability. UAM will demand that its navigation systems offer dm-accurate positioning with integrity risk on the order of 10e-7 for a meter-level alert limit and availability with several more 9s than 99.9%
This proposal’s work in PNT protection is best viewed as part of a comprehensive navigation and timing solution concept called deep-layered navigation (DLN) in which synergistic but independent navigation systems are layered to increase accuracy and robustness. DLN is the navigation analog of the “defense in depth” concept in information security, where multiple layers of security controls and checkpoints are emplaced throughout a system such that even when some layers are breached, security is maintained. Likewise, in the safety-of-life UAM navigation context, multiple layers of navigation and timing systems, all interoperable and mutually-reinforcing but substantially independent, are an essential defense against the whims of Mother Nature and the foibles of human nature.
At DLN's core sits redundant inertial navigation, which is virtually impervious to radio frequency (RF) interference, poor weather, signal blockage, and data ambiguity. The outermost layer---the default navigation system and first line of defense---is a specialized variant of inertially-aided CDGNSS, recently developed in the UT Radionavigation Laboratory that has been substantially secured against spoofing and substantially hardened against the multipath and signal blockage conditions of the urban ground vehicle environment, which can be considered a worst-case realization of the urban air vehicle environment. But despite its coupling with inertial sensing, the technique developed in the UT RNL cannot tolerate extended GNSS outages. A secondary source of absolute PVT is required to bound the growth of position errors.
Fusion of GNSS and signals of opportunity (SoPs) has been explored by the CARMEN+ team with promising results. For a safety-of-life application like UAM, SoPs will be viewed less favorably than a dedicated system such as GNSS. But SoP availability, ample bandwidth, and strong received power nonetheless make them an appealing component of any deep-layered navigation and timing strategy. Compared to terrestrially-sources SoPs, SoPs from LEO mega-constellations such as SpaceX's Starlink, Amazon's Kuiper, and OneWeb enjoy the advantage of vastly broader---indeed, nearly global---coverage. The downlink signals from these constellations are also carried on higher frequencies (Ku-band for Starlink and OneWeb; Ka-band for Kuiper), whose short wavelengths permit use of small phased-array receiver antennas capable of forming narrow (few degrees) beams toward their serving satellites. Narrow beamforming makes such systems naturally resilient to radio interference.
The research team proposes to develop novel signal capture, processing, and estimation techniques that will enable extraction of navigation and timing measurements from broadband mega-LEO signals of opportunity. The research team envisions broadband LEO-based PNT as a novel and useful component in a comprehensive deep-layered navigation and timing system for civil transportation and infrastructure. GNSS receivers in Low Earth Orbit (LEO) are a proven asset for detecting, classifying, and geolocating terrestrial GNSS interference that can be a danger to civil aviation, maritime, or ground vehicle traffic. Emitter geolocation from LEO offers worldwide coverage with a frequent refresh rate, making it possible to maintain a common operating picture of terrestrial sources of interference. The research team proposes to further develop and demonstrate techniques whereby GNSS interference signals captured by a single satellite or by multiple satellites can be used to characterize and geolocate the signals’ source. The research team will pursue single-satellite, multi-satellite-indirect, and multi- satellite-direct techniques to obtain geolocation solutions optimized for context and performance. The research team's focus in this next year will be on single-satellite geolocation solutions.
]]></description>
      <pubDate>Thu, 21 Nov 2024 17:26:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458990</guid>
    </item>
    <item>
      <title>Improving GNSS Resiliency Using Edge AI Solutions</title>
      <link>https://rip.trb.org/View/2444693</link>
      <description><![CDATA[Improving the resilience of Global Navigation Satellite Systems (GNSS) is crucial, especially in challenging environments where global positioning systems (GPS) signals can be weak or disrupted. Enabling Edge Artificial Intelligence (Edge AI) offers promising solutions that employ AI algorithms and models on edge devices without constant reliance on cloud infrastructures; especially in highly dense blockage environments, is an interesting area of research. Our strategy will explore how Edge AI can enhance GNSS resiliency in challenging scenarios by bringing intelligence to the edge node. 

  

In dense urban environments, where reliable measurements are often inaccessible due to obstacles, Edge AI techniques can play a crucial role. When deploying Edge AI in dense blockage environments, such as urban canyons or indoor spaces, we will consider the following: 

  

1.    Edge Computing Infrastructure for GNSS Services: 

Setting up edge servers or devices close to the GNSS receivers to process AI algorithms locally. This minimizes latency and ensures real-time decision-making. 

Opting for low-power, compact edge devices that can handle AI workloads efficiently. 

2.    AI Algorithms for Signal Enhancement: 

Employing machine learning models to enhance GNSS signals affected by multipath reflections, interference, and blockages. 

Techniques like deep learning, Kalman filtering, and particle filters will be investigated for positioning accuracy enhancement by mitigating noisy measurements. 

3.    GNSS Abnormalities Detection and Mitigation 

Create a more robust GNSS system 

Employe multiple positioning sources for redundancy and come up with the concept of multi- level accuracy support 

Employe AI for GNSS abnormalities detection, abnormal behavior includes attacks and failures 

Come up with GNSS abnormalities mitigation techniques 

4.    Cooperative Learning 

Allow devices to share positioning information and learn in a cooperative approach using distributed learning 

This will allow low end devices to benefit from more capable devices with higher accuracy GNSS support 

5.    Map-Based Localization: 

Pre-existing maps will be leveraged, or local maps of the environment will be created. These maps can help in predicting GNSS signal blockages and aid in localization. 

Techniques like SLAM (Simultaneous Localization and Mapping) will be considered. 

6.    Dynamic Adaptation and Hybrid Positioning (Coexistence Networking Strategies (CNS): 

Implementing adaptive algorithms that adjust parameters based on real-time conditions. 

Hybrid positioning approaches provide redundancies and improve availability in challenging environments. For example, if a GNSS signal is blocked due to tall buildings, the system will be complemented by additional technologies such as Wi-Fi or Bluetooth for localization. 

This project will employ Edge AI solutions that are tailored to the challenging environments and use cases. Regular testing and validation will be implemented to ensure reliable performance. ]]></description>
      <pubDate>Tue, 22 Oct 2024 16:32:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2444693</guid>
    </item>
    <item>
      <title>Development of a Generalized Integrity Monitoring Framework For CAV Application</title>
      <link>https://rip.trb.org/View/2444697</link>
      <description><![CDATA[CAV applications can be broadly classified into three major categories: safety, mobility, and environmental. Mobility and environmental applications require a coarse positioning accuracy (5-10 m) and lane-level positioning accuracy (< 1 m), while safety-critical applications require a where-in-lane level accuracy (< 0.2 m). Uncertainty in positioning information can sabotage driving functionalities and cause a safety concern. Position uncertainty of a vehicle can be attributed to the sensor suite available on the vehicle, along with any additional external sensor information that may aid the in-vehicle sensors. Furthermore, a fully connected and Automated vehicle (CAV) may turn itself into a degraded CAV, AV-only, CV-only, or Human-driven vehicle (HDV) while experiencing communication or control loss. The type, quantity, placement, and measurement uncertainty of sensors play a great role in determining the navigation performance of a vehicle. Additionally, in a mixed traffic scenario, there exist various types of positioning solutions, vehicles, sensor modalities, communications capabilities, and applications. It is unlikely that two vehicles having the same set of sensors and computation hardware will output similar navigation performance. Therefore, it is important to study and analyze the integrity of positioning systems under various conditions. 

Integrity monitoring (IM) methods have been extensively studied and developed for in-vehicle sensor systems primarily consisting of GNSS receivers (e.g. RAIM) often integrated with IMUs, vehicle odometry, and perception sensors such as radar, camera, and LiDAR. The localization performance and safety of a vehicle have mainly been studied from an ego vehicle's perspective focusing on enabling automated driving functions through onboard sensors and compute platform. Further, there is a surge in V2V/V2I/V2P/V2X research focusing on information sharing between road agents for improved positioning, navigation, and control. Given the number of sensor sources and the amount of data shared between road agents, it is imperative to develop integrity monitoring frameworks for cooperative scenarios. Based on the research team's initial literature survey, despite the growing interest in this area, there is a noticeable gap in the current literature that addresses the "cooperative-IM (integrity monitoring)" framework, indicating the need for further research to propose new Required Navigation Performance (RNP) parameters that may support CAV applications.  

As a part of the research team's smart intersection projects (City of Riverside & City of Rialto), it has conducted various Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based cooperative positioning experiments, where perception data from both onboard and roadside sensors i.e. camera, LiDAR, is shared among the vehicles and infrastructure through C-V2X communication. The research team has successfully demonstrated improved vehicle detection and positioning accuracy (0.3 meters with Roadside LiDAR). Furthermore, the research team is currently experimenting with Cohda wireless MK6 modules to transmit basic safety messages (BSMs) and other information among vehicles in a V2V setting. However, the question remains whether the empirically determined Required Navigation Performance (RNP) values hold consistent across different driving conditions, positioning hardware, and communication topologies. Inconsistencies in RNP parameters would lead to the failure of CAV applications. ]]></description>
      <pubDate>Tue, 22 Oct 2024 16:21:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2444697</guid>
    </item>
    <item>
      <title>Examining and Enhancing Vehicle Spoofing Detection Capabilities in CAV Applications: Real-World Testing </title>
      <link>https://rip.trb.org/View/2444698</link>
      <description><![CDATA[This project addresses the rising incidence of GNSS spoofing attacks by enhancing detection capabilities in Connected and Autonomous Vehicles (CAVs). The focus is on leveraging information shared by surrounding vehicles and signals of opportunity to identify jamming and spoofing threats during vehicle-to-vehicle maneuvers. Conducted in collaboration with the CARNATIONS team at Illinois Tech and StarNav, the project explores detection strategies using real-world spoofing data and onboard sensors such as LiDAR and cameras.

The research is divided into two tasks: (1) detecting GNSS meaconing, which involves analyzing the impact of signal retransmission delays on non-target vehicles and identifying key detection information, such as timing and signal direction, and (2) detecting targeted GNSS spoofing attacks, particularly in scenarios where the lead vehicle of a platoon is compromised, focusing on the detection and mitigation of manipulated satellite data.

The team will develop a layered defense strategy, implementing detection systems through hardware-in-the-loop simulations and real-world testing to explore vehicle re-localization during attacks. This research aligns with the U.S. Department of Transportation’s priority of enhancing transportation system safety and resilience by countering GNSS spoofing threats in CAV applications, particularly in highway platooning.]]></description>
      <pubDate>Tue, 22 Oct 2024 16:11:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2444698</guid>
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    <item>
      <title>Resilient V2X Communication for Cooperative and Remote Driving </title>
      <link>https://rip.trb.org/View/2444700</link>
      <description><![CDATA[This project focuses on developing a Global Navigation Satellite System (GNSS) spoofing detection and mitigation system for Connected and Autonomous Vehicles (CAVs) by leveraging information shared between vehicles and other signals of opportunity. The main goal is to detect jamming and spoofing attacks, particularly in vehicle-to-vehicle scenarios where cooperative operations are targeted. In collaboration with Illinois Tech's CARNATIONS team and StarNav, the project will utilize real-world data and onboard sensors such as LiDAR and cameras to enhance detection capabilities.

The research is structured into two tasks: (1) detecting GNSS meaconing, where attackers retransmit GNSS signals with a delay, misleading vehicles about their position and timing, and (2) identifying and mitigating targeted GNSS spoofing attacks, where attackers aim to control the navigation system of a lead vehicle in a platoon. These tasks will involve analyzing the spoofing effects on non-target vehicles and determining key detection information, such as signal arrival time and direction.

The detection system will be incrementally developed through hardware-in-the-loop simulations and real-world data analysis, exploring the potential for re-localization of affected vehicles during spoofing attacks. The project aligns with U.S. Department of Transportation priorities by enhancing transportation safety and resilience, particularly in highway platooning scenarios. The outcome will be a robust CAV application that remains resilient against GNSS spoofing threats.






]]></description>
      <pubDate>Tue, 22 Oct 2024 16:08:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2444700</guid>
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