<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>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>
    </item>
    <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>
    </item>
  </channel>
</rss>