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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxODA0IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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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>
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    <item>
      <title>Elevate the Prioritization of Fleets and Equipment Within an Overall Asset Management Strategy</title>
      <link>https://rip.trb.org/View/2712185</link>
      <description><![CDATA[State department of transportation (DOT) fleets support highway maintenance, emergency response, and agency operations. However, fleet funding and prioritization can vary across agencies and may differ from approaches used for infrastructure assets such as roads and bridges. Funding constraints may contribute to increased lifecycle costs, additional maintenance requirements, and challenges related to fleet expansion and replacement planning. In some cases, fleet-related needs compete with other agency priorities during funding and resource allocation decisions.

Transportation Asset Management Plans (TAMPs), established under the Moving Ahead for Progress in the 21st Century Act (MAP-21), provide a framework for managing infrastructure assets using lifecycle-based approaches. Although these plans have traditionally focused on roads and bridges, some agencies are exploring ways to incorporate fleet assets into asset management practices. Current approaches vary across agencies, and practices for integrating fleet management into TAMPs are still evolving. In addition, fleet performance and utilization data may not always be fully integrated into planning and budgeting processes. Research is needed to identify critical equipment, performance measures, funding strategies, and data management methods to assist state DOTs with assessing fleet investment needs and managing fleet resources effectively.

The objectives of this research are to (1) develop a guide with reporting tools and successful practices for prioritizing fleet and equipment within state DOT asset management planning processes to improve lifecycle management, and (2) develop a framework for a Fleet Asset Management Plan (FAMP) to aid state DOT funding decisions.]]></description>
      <pubDate>Tue, 09 Jun 2026 16:07:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712185</guid>
    </item>
    <item>
      <title>SPR-5042: Performance and Safety Evaluation of Truck Mounted Debris Clearing Systems</title>
      <link>https://rip.trb.org/View/2709430</link>
      <description><![CDATA[The principal investigators will help the Indiana Department of Transportation (INDOT) evaluate truck-mounted debris clearing systems by achieving the following three main objectives: 1) Development of an event-triggered, multi-sensor data collection framework integrating multi-camera video and Global Positioning System (GPS) to enable automated, machine vision-based performance assessment. 2) Quantitative evaluation of system performance through field testing to measure debris removal effectiveness, roadway interaction, and operational efficiency across real-world conditions. 3) Assessment of safety and traffic impacts by analyzing worker exposure, operational risks, and vehicle interactions to quantify how these systems influence roadway safety and deployment practices.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:31:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709430</guid>
    </item>
    <item>
      <title>SPR-5007: Performance Evaluation and Development of Cost-Effective DWPT Pavements</title>
      <link>https://rip.trb.org/View/2709428</link>
      <description><![CDATA[The proposed research work will address key questions that remain among policymakers, fleet operators, investors, and vehicle manufacturers regarding the cost, performance, and viability of Dynamic Wireless Power Transfer (DWPT) technologies. This effort will lay the groundwork for formalizing largescale public-private partnerships necessary to support the deployment of multi-mile DWPT corridors at both interstate and intrastate levels.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:25:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709428</guid>
    </item>
    <item>
      <title>Enhancing Safety in Mixed-Autonomy Traffic via Prediction-Based Connected Autonomous Vehicle Control</title>
      <link>https://rip.trb.org/View/2703928</link>
      <description><![CDATA[This project proposes a new framework for prediction-based connected autonomous vehicle (CAV) control to enhance safety in mixed-autonomy traffic where CAVs and human-driven vehicles (HVs) coexist. Specifically, by predicting future traffic conditions behind a target CAV, the vehicle can be proactively controlled to improve the safety and efficiency of the overall traffic stream. This approach is motivated by the fact that a controlled CAV directly influences the behavior, safety, and performance of following HVs through car-following interactions. Accordingly, the proposed method jointly considers a CAV and its following HVs in the design of a safety-aware driving strategy. Although HVs do not communicate with CAVs, traffic states related to HVs can be estimated using partial traffic measurements collected by CAVs. Leveraging these predictions, the proposed control strategy will be formulated within a model predictive control (MPC) framework to improve safety and traffic efficiency for HVs following a CAV. Extensive simulation studies will be conducted under a range of traffic scenarios and HV driving styles to demonstrate the effectiveness of the proposed approach. In addition, multiple CAV penetration rates will be evaluated to examine scalability and deployment potential. 
This project is highly aligned with the Mid-America Transportation Center's (MATC’s)  mission to advance transportation safety through technology development, technology transfer, and deployment. It addresses a timely safety challenge: near-term traffic will be mixed-autonomy, where early-generation autonomous vehicles (e.g., ACC-equipped vehicles and emerging CAVs) operate alongside the majority of HVs. In this environment, safety risks arise not only from individual vehicle performance, but also from interactions between automated and human drivers—an issue that is often overlooked in existing CAV control design.
While this project will leverage an existing dataset collected in Minnesota and high-fidelity simulation data generated in Simulation of Urban MObility (SUMO) for numerical investigation and validation, the team anticipates extending the proposed methodology in future work using connected-vehicle and SPaT data to be collected by Dr. Li Zhao’s team at UNL, in collaboration with the Nebraska DOT. 
]]></description>
      <pubDate>Thu, 21 May 2026 22:41:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703928</guid>
    </item>
    <item>
      <title>New Models and Solutions to Vehicle Routing with Cardinality and Distance Constraints</title>
      <link>https://rip.trb.org/View/2703788</link>
      <description><![CDATA[Many emerging transportation and logistics operations are constrained by both the maximum distance a vehicle can travel and the number of customers it can serve before requiring replenishment, recharging, or maintenance. These operational realities motivate the need for new routing optimization models that explicitly integrate distance and cardinality constraints. This project proposes the first comprehensive study of a novel Black-and-White Vehicle Routing Problem (BWVRP), where customer nodes and replenishment nodes are jointly routed across a fleet of vehicles, with replenishment nodes allowed to be visited multiple times. The project will develop new mixed-integer linear programming models and exact branch-and-cut methods to obtain optimal solutions for small and medium-sized instances. To address large-scale instances, efficient heuristic and metaheuristic algorithms will be designed and implemented. In addition to methodological advances, the project will develop a data-driven optimization decision-support tool integrating models, algorithms, and user-friendly interface. 
]]></description>
      <pubDate>Sat, 16 May 2026 11:45:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703788</guid>
    </item>
    <item>
      <title>Driver Takeover and Shared-Control Collaboration in Automated Driving Systems under Rural Conditions: Evaluating Real-Time Cognitive Responses in Field and Simulated Settings</title>
      <link>https://rip.trb.org/View/2703691</link>
      <description><![CDATA[This proposal outlines a multidisciplinary research initiative to assess drivers'
physiological and cognitive workload, stress levels, emotional states, and trust during takeover
performance in human-machine co-driving systems using an automated driving simulator. In specific,
three research objectives are to (1) Designing realistic and validated driving scenarios in simulators;
(2) Validating physiological sensors for measuring driver physiological responses; and (3)
Developing cognitive and situational awareness-based decision-making framework. To achieve these
objectives, simulator-based data will be collected, along with test drivers incorporating physiological
sensors while in the driving tests. Existing open-source data will be applied to expanding traffic
scenarios.
]]></description>
      <pubDate>Fri, 15 May 2026 14:19:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703691</guid>
    </item>
    <item>
      <title>Phase II Pilot Program for UAS-Enabled Component Level Bridge Inspection in New Mexico</title>
      <link>https://rip.trb.org/View/2703713</link>
      <description><![CDATA[Building on the success of Phase I, Phase II of the project seeks to expand unmanned aircraft system (UAS) inspection capabilities to focus on bridge superstructures. This is a more complex and critical component of overall structural performance, because superstructures, comprising elements such as girders, beams, and trusses, are responsible for transferring deck loads to substructures and ultimately to the ground. Their integrity is essential for bridge safety and serviceability.]]></description>
      <pubDate>Fri, 15 May 2026 13:14:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703713</guid>
    </item>
    <item>
      <title>TraCR Foundational Project: TraCR Collective Transportation Cybersecurity Testbeds</title>
      <link>https://rip.trb.org/View/2697454</link>
      <description><![CDATA[The National Center for Transportation Cybersecurity and Resiliency's (TraCR's)
foundational project aims to develop technological tools, prototypes, testing platforms, and facilities to ensure the cybersecurity and cyber-resilience of multimodal transportation systems and related infrastructure. The project is led by Clemson University (Clemson) under the strategic direction of Dr. Ronnie Chowdhury (Lead PI), with coordination support from Dr. Sabbir Salek (Co-PI), and involves all eight other TraCR partner institutions organized into four subgroups. A structured project governance framework, including biweekly subgroup meetings, monthly full-team coordination meetings, quarterly progress reporting and advisory board engagement, ensures alignment with project milestones, integration across teams, and effective monitoring of technical progress and deliverables. 

Clemson collaborates with Benedict College (Benedict), South Carolina State University (SCSU), and the University of Texas at Dallas (UTD) to advance a comprehensive, automated threat modeling capability for multimodal transportation systems. Building on the Transportation Cybersecurity and Resiliency Threat Modeling Framework (TraCR-TMF), the team conducts testbed-in-the-loop evaluations within Clemson’s real-world cybersecurity testbed, implementing digital-twin-based cybersecurity analysis of in-vehicle networks, and engaging state transportation agencies to assess operational transferability. Additionally, the team will work to integrate graph-based reasoning models into threat modeling, deploy supervised ModernBERT classifiers, and align with the MITRE Embedded Systems Threat Matrix to strengthen structured system-to-vulnerability mapping and improve threat coverage across transportation cyber-physical systems.

The other partner institutions will develop additional real-world and virtual testing platforms to support cybersecurity experimentation for multimodal transportation. Florida International University (FIU) and the University of Alabama at Tuscaloosa (UA) are jointly advancing the Open-Source Connected and Automated Mobility Co-Simulation (OpenCAMS) environment and related simulation platforms, integrating SUMO, CARLA, and network simulation tools, to evaluate privacy-aware multimodal large language models and post-quantum-secure C-V2X communications. Their efforts further include the development and validation of spoofing attack models targeting Basic Safety Message transmissions and multi-frequency GPS receivers, as well as investigations into backdoor-resilient perception systems and the security of vision-language models for intelligent transportation applications.

Purdue University (Purdue) and the University of California, Santa Cruz (UCSC) are advancing adversarial testing methodologies through integrated physical-virtual experimentation frameworks that combine miniature autonomous vehicle testbeds, CARLA/METS-R simulation coupling, and scenario-based vulnerability discovery. These activities include simulation-to-real validation of perception and traffic signal spoofing attacks, evaluation of V2X safety message vulnerabilities, cybersecurity analysis of shared micromobility Bluetooth pairing protocols, implementation of lightweight post-quantum cryptographic protections for vulnerable road user beacons, and closed-loop security assessments of traffic signal controller infrastructures, along with investigations of secure multimodal AI agents and memory-augmented reasoning architectures for autonomous robotic transportation systems.

In addition, Morgan State University (MSU) is enhancing its connected vehicle cybersecurity experimentation capabilities by developing replay-attack models targeting C-V2X onboard units and evaluating mitigation strategies in its real-world testbed environment, in collaboration with Clemson. These efforts quantify communication-level impacts on safety-critical applications and support the development of deployable countermeasures to strengthen resilience against wireless attack vectors affecting connected transportation infrastructure.
]]></description>
      <pubDate>Thu, 30 Apr 2026 12:19:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697454</guid>
    </item>
    <item>
      <title>A Novel Hybrid Attack Model and A Quantum-Infused Hybrid Defense Method for Resilient Perception of Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2697419</link>
      <description><![CDATA[This project strengthens the cybersecurity and resiliency of camera-based perception for autonomous vehicles by addressing two fast-growing attack classes: universal adversarial perturbations (UAPs) and generative/deepfake-style scene manipulations that can add, alter, or remove objects in the camera feed. The team will first build and validate a novel hybrid attack that combines image-agnostic UAP noise with generative object-disappearance attacks (ODAs) using real-time inpainting to create “hallucination” driving scenes where objects are misclassified or vanish entirely. The project will also develop a quantum-enhanced hybrid defense that fuses parameterized quantum circuits with classical deepfake/manipulation detection, leveraging quantum–classical disagreement and out-of-distribution signals to robustly detect both pixel-level perturbations and semantic object edits. The project will produce deployable prototypes: (1) a real-time hybrid “malware” attack pipeline and (2) a quantum-infused hybrid detector, which will be evaluated in realistic AV scenarios and deployed for testing on connected-vehicle testbeds (e.g., Clemson University Connected Vehicle Testbed or CU-CVT and Morgan State).

]]></description>
      <pubDate>Thu, 30 Apr 2026 12:17:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697419</guid>
    </item>
    <item>
      <title>Guiding Electronic Control Unit (ECU) Firmware Fuzzing with Hardware-Level Side-Channel </title>
      <link>https://rip.trb.org/View/2697290</link>
      <description><![CDATA[This project develops a novel electromagnetic (EM) side-channel-guided fuzzing framework for automotive Electronic Control Unit (ECU) firmware security testing. The approach addresses key challenges in ECU security research, namely that firmware is often encrypted, proprietary, and tightly coupled to hardware, making traditional instrumentation-based fuzzing impractical. By capturing and analyzing EM emanations from ECUs during execution, the framework estimates code coverage without requiring firmware modification, instrumentation, or rehosting. The system integrates this EM-based coverage feedback into a fuzzer to guide test case generation via Controller Area Network (CAN) bus communication. The project will conduct extensive fuzzing campaigns on real automotive ECUs from various manufacturers to discover zero-day vulnerabilities and enhance vehicle cybersecurity. ]]></description>
      <pubDate>Wed, 29 Apr 2026 16:47:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697290</guid>
    </item>
    <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>Artificial Intelligence (AI)-Enabled Post-Quantum Cryptography for Real-World Deployment of Secure and Resilient Communication for Intelligent Transportation Systems</title>
      <link>https://rip.trb.org/View/2696971</link>
      <description><![CDATA[Cellular Vehicle-to-Everything (C-V2X) communication, standardized in 3GPP Release 14/15 PC5 sidelink mode, is the US DOT-approved technology for direct V2V (vehicle-to vehicle)/ V2I (vehicle-to-infrastructure) communications in the 5.9 GHz band. Current standards and specifications (e.g., SAE J3161 and USDOT/ITE RSU requirements) mandate PC5 Mode 4 operation to enable interoperable safety messaging using conventional cryptographic methods, such as Elliptic Curve Cryptography (ECC). However, existing cryptographic methods are vulnerable to quantum-computing-based attacks. Thus, integrating Post-Quantum Cryptography (PQC) into C-V2X communication is imperative to ensure future resilience. However, National Institute of Standards and Technology (NIST)-standardized PQC algorithms introduce large key sizes and computational complexity, resulting in significant latency and bandwidth overhead. These effects risk violating the 100-ms end-to-end delay requirement for 10 Hz Basic Safety Messages (BSMs) and can congest the 5.9-GHz safety channel. Moreover, the direct integration of PQC into current communication standards, such as IEEE 1609.2 and ETSI, poses challenges because these frameworks were originally designed for lightweight ECC-based operations. 
Similarly, post-quantum Homomorphic Encryption (HE) offers robust privacy protection by allowing computation directly on encrypted data without decryption; however, its high computational cost and ciphertext expansion currently limit its use in latency-critical V2X and infrastructure-to-infrastructure (I2I) scenarios. Therefore, deploying PQC and HE within operational testbeds demands optimized scheduling, resource allocation, and adaptive algorithm management to balance cryptographic strength with real-time constraints. To address these challenges, this project aims to develop and evaluate artificial intelligence (AI)-enabled PQC through real-world prototype implementation and testbed integration, thereby enabling the real-world deployment of secure and resilient communication in intelligent transportation systems. Specifically, the objectives of this project are: (i) implementation and real-world evaluation of an AI-enabled PQC integration and dynamic switching framework for C-V2X communication; (ii) real-world evaluation of a privacy-preserving roadside unit (RSU)-Cloud (I2C) communication pipeline using post-quantum homomorphic encryption; and (iii) development of a federated learning framework for collaborative PQC selection policies. To address the USDOT and TraCR 2025–2026 priorities, this project emphasizes field-tested prototypes and operational validation, rather than simulation-only evaluation, to ensure deployment relevance. This project will directly contribute to the deployment of PQC-enabled V2X communication for a secure and reliable connected transportation system.
]]></description>
      <pubDate>Wed, 29 Apr 2026 16:39:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696971</guid>
    </item>
    <item>
      <title>Resilient Software-Defined Vehicle Platform Architectures with Secure Live Migration</title>
      <link>https://rip.trb.org/View/2696966</link>
      <description><![CDATA[Modern vehicles use software-defined vehicle (SDV) platforms that integrate functions via virtualization, but current designs lack resiliency against security incidents or hardware obsolescence. This project aims to enhance vehicle security by developing and evaluating secure live migration techniques for virtual machine (VM)-based workloads. By allowing actively running services to move between electronic control units (ECUs) without interruption, the project enables real-time upgrades and mitigation of cyberattacks within next-generation zonal architectures.

]]></description>
      <pubDate>Wed, 29 Apr 2026 16:36:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696966</guid>
    </item>
    <item>
      <title>Investigating Driver Behavior Under Cyberattacks in Connected Vehicle Environments: Phase II</title>
      <link>https://rip.trb.org/View/2696964</link>
      <description><![CDATA[Phase II will examine driver behavior and decision-making under cyberattacks in connected-vehicle contexts using high-fidelity, human-in-the-loop driving simulators at Morgan State University (urban) and Clemson University (suburban). The team will develop reusable Cyberattack Injection Modules (CIMs) for UCWinRoads and SimCreator and validate one vehicle-centered attack (false blind spot warning) and two infrastructure-centered attacks (falsified signal phase-and-timing and “phantom” signal-ahead information). The study will leverage IRB-approved human-subject testing, conduct a Maryland MVA pilot demonstration, and curate a Standardized Attack Scenario Library (ASL) for replication and training use.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696964</guid>
    </item>
    <item>
      <title>Cyber-CAT: Prototyping and Experimental Demonstration of Cyberattack Mitigation in Connected and Automated Traffic (CAT)</title>
      <link>https://rip.trb.org/View/2696946</link>
      <description><![CDATA[The need for research on experimental prototyping and demonstration of cyberattack mitigation strategies in Connected and Automated Traffic (CAT) is critical as modern transportation systems increasingly depend on interconnected and automated vehicle operations. With the growing adoption of Vehicle-to-Everything (V2X) communication, the interactions and cascading effects that arise within real connected traffic systems caused by cyberattacks need to be investigated in a timely manner. 
The proposed project will deliver an integrated experimental framework and multi-layer mitigation strategy to strengthen cybersecurity in CAT systems. Key highlights include: Cyber-CAT System Development: Establish a real and virtual hybrid testbed integrating CAVs, infrastructure, and C-V2X communication to enable realistic testing of cyber threats and countermeasures; Multi-layer Mitigation: Prototype and validate Zero-Trust-LLM-assisted mitigation and artificial intelligence (AI)-based intention sharing mitigation to maintain safety under attack conditions; and Guidance and Knowledge Transfer: Produce data and design guidelines to inform US DOT and industry on C-V2X security standards and resilient corridor deployment.
]]></description>
      <pubDate>Wed, 29 Apr 2026 12:27:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696946</guid>
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