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    <title>Research in Progress (RIP)</title>
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    <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>
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      <title>Research in Progress (RIP)</title>
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    <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>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>Development Of Architecture For Generative Pre-trained  
Transformer (Gpt) Inspired Model For Bridge Engineering  
With Application To Service Life Design, Called Bridgegpt. </title>
      <link>https://rip.trb.org/View/2404259</link>
      <description><![CDATA[A Generative Pre-trained Transformer (GPT) system can, if coupled with data, make a paradigm shift in approaches to bridge design and construction. The research proposed in this project focuses on the development of BridgeGPT architecture, using bridge service life design as the first application. This is in keeping with US DOT Secretary of Transportation upper administration interest in the development of BridgeGPT, as expressed at the IBT/ABC-UTC kickoff meeting in May, 2023. At that meeting, the concept of BridgeGPT was included in the power point presentation. The SHRP2 R19A project has gathered a wealth of data on bridge service life design. However, the full potential of this data has yet to be utilized. Traditional methods of bridge design and maintenance, while reliable, can benefit greatly from the predictive power and adaptability of models like BridgeGPT. There is a crucial need to more closely relate cutting edge artificial intelligence (AI) technology and bridge engineering to optimize service life design, reduce costs, and enhance infrastructure safety. This project aims to utilize the capabilities of GPT-like models, adapt them for bridge engineering, and create a tool that learns and improves over time. ]]></description>
      <pubDate>Sun, 21 Jul 2024 14:49:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2404259</guid>
    </item>
    <item>
      <title>Exploring Hybrid Architecture for Data Processing/Storage Needs of FDOT</title>
      <link>https://rip.trb.org/View/2384742</link>
      <description><![CDATA[Objective 1: The first step towards moving to a hybrid cloud architecture is to understand the basics of the cloud technology, encompassing operational mechanisms, key characteristics, and diverse service models. The research team will study and document different cloud services, explore mixed storage architectures, data localization per Cloud Procurement and Contractual Elements, Rule 60GG-4.002(6), Florida Administrative Code, and analyze the potential benefits in terms of efficiency, security, and feasibility. The goal is to provide useful insights and knowledge to help Florida Department of Transportation (FDOT) Transportation Systems Management and Operations (TSM&O) choose the best cloud services and storage setups for its needs. With this detailed documentation, FDOT will be better equipped to make informed decisions and use cloud technologies to improve its operations. Objective 2: The second objective is to thoroughly examine and analyze other DOTs’ practices regarding migration to cloud-based services. This will result in valuable insights that can guide FDOT TSM&O’s move to cloud-based services. By closely studying the experiences, best practices, challenges, and lessons learned from these agencies, the research team aims to enhance the efficiency, security, and effectiveness of FDOT TSM&O’s cloud-based initiatives. The primary aim is to provide a comprehensive overview of cloud adoption strategies, service models, deployment scales, security considerations, and associated challenges, ultimately informing and enriching FDOT’s cloud migration endeavor. Objective 3: This objective aims to develop a holistic framework that facilitates the successful migration of applications to cloud-based environments. The informed decision-making in transitioning legacy and new applications to the cloud environment should consider various aspects, including cost analysis for both importing and exporting for each cloud provider, technology compatibility, functionality, latency, recoverability, exit strategies, and security. This objective encompasses the creation of a hybrid cloud migration decision framework tailored to evaluate applications for cloud migration suitability. Furthermore, this involves designing a cloud migration and change management plan, outlining phased processes, from data migration to testing and validation. The training guidelines will empower existing personnel, bridging the knowledge gap while transitioning from legacy platforms to cloud technology and ensuring efficient and effective utilization of the newly adopted cloud-based services.]]></description>
      <pubDate>Mon, 03 Jun 2024 14:13:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2384742</guid>
    </item>
    <item>
      <title>A Zero Trust Architecture for Secure Connected and Autonomous Vehicles.</title>
      <link>https://rip.trb.org/View/2335051</link>
      <description><![CDATA[Connected and Autonomous Vehicles (CAVs) are the future of personal and public transportation. As CAVs increasingly rely on cyber-based control, navigation, and communication, security has become a pressing concern in future transportation systems. The complexity and inter-connectedness of CAVs offer myriad opportunities for security compromise, potentially resulting in unsafe operation or leakage of confidential information about the user. Zero Trust Architectures (ZTA) for networks have emerged as a fundamentally new way of approaching security. It offers new paradigms for defining and enforcing policy through various means rooted in modeling trust relationships. The zero-trust security model does not automatically trust any user or device inside or outside the network perimeter. Instead, it enforces a set of policies (i.e., rules that are dynamically maintained and enforced) to verify and ensure the security of resources. ZTA can aid in reducing potential risks in CAVs by guaranteeing that only approved users and devices can access sensitive systems and data. This project will investigate how ZTA can be adapted to CAVs to provide fundamental protection for individual components within CAV systems and their supporting infrastructure. ]]></description>
      <pubDate>Fri, 09 Feb 2024 19:37:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2335051</guid>
    </item>
    <item>
      <title>Secured Small-Key-Based Post Quantum Cryptographic Scheme for Blockchain-based VANET</title>
      <link>https://rip.trb.org/View/2335053</link>
      <description><![CDATA[Blockchain-based Vehicular Ad-hoc Network (VANET) architecture has been gaining popularity due to its distributed and decentralized architecture, efficient data transmission capability, and secure data generation and broadcasting ability over VANET networks. Rating-based or trust-value-based blockchain networks can efficiently play a trusted role by setting up the proof-of-work or proof-of-stake consensus mechanisms. Such a trust management system could ensure privacy-protected and secured vehicle-to-everything communication because of its ability to ensure the veracity of the exchanged messages via a digital signature of a message sender (e.g., vehicle). However, due to the high mobility of vehicles, small key-based encryption is necessary in VANET as it requires less complex computational operations and storage. 
Existing studies prove that a non-quantum computing-based or classical attack cannot generate a cyber attack on blockchain-based VANET because blockchain can identify the attacker through consensus-based or rating-based mechanisms, hashing, encryption, and its distributed nature with transparency in the public ledger-based approach. The blockchain-based architecture relies on two cryptographic mechanisms to provide security and trust: (1) check the integrity of the data itself using hash functions, and (2) check the ownership of the data with asymmetric cryptography. However, if a quantum algorithm can break the hash function or the cryptographic algorithm, it can create security concerns for any secure communication architectures, such as blockchain, as it uses an encryption technique (mostly on subgroup-finding algorithms utilizing factorization and discrete logarithm), e.g., Rivest-Shamir-Adleman and elliptic curve digital signature algorithms. On the other hand, although prior studies have been conducted on improving the ownership mechanism of blockchain and making it quantum-safe through post-quantum cryptography and quantum key distribution, post-quantum cryptography suffers from periodicity and symmetry. It uses large-size keys, which increase the complexity of the decryption of the key, such as a lattice-based architecture. Hash-based cryptography and multivariate cryptography exhibit a drawback in large signature sizes, leading to a larger block size and, consequently, larger memory size. Similarly, code-based cryptography encounters the issue of increasing complexity due to larger key sizes, demanding extensive memory storage, and the risk of decoding failures when utilizing smaller keys in specific scenarios.  Therefore, a novel lightweight Post Quantum Cryptographic (PQC)  solution, which could adapt to the dynamic VANET scenario and ensure security against quantum-based attacks, is needed according to the US NIST’s cybersecurity framework.
The overarching goal of this project is to develop a new small key-based PQC solution, the Diophantine Isogeny Key Exchange (DIKE) scheme, for VANET to ensure security against quantum-based attacks. Specifically, the objectives of this project are to (1) develop and implement a quantum-based attack model utilizing both quantum Shor’s and Grover’s algorithms on a blockchain-based VANET, which will highlight the need for a quantum-secured blockchain and (2) formulate a new PQC solution, DIKE, which relies on the integration of Diophantine equations and isogenies to provide a secure key exchange mechanism that is resilient against quantum attacks.
]]></description>
      <pubDate>Fri, 09 Feb 2024 19:33:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2335053</guid>
    </item>
    <item>
      <title>Hybrid classical-quantum AI approach for detecting cyberattacks in vehicles</title>
      <link>https://rip.trb.org/View/2335054</link>
      <description><![CDATA[In this project, the research team plans to develop a hybrid classical-quantum machine learning library to detect vehicle cyberattacks. By leveraging quantum supremacy, the team's library should improve the speed of training and the accuracy of intrusion-detection systems. Specifically, the team will analyze the performance of the quantum neural network in the feature extraction and the feature analysis, respectively. After understanding this performance, the team will find a hybrid classical-quantum architecture that generates the best performance. In addition, the team will test their hybrid library in different quantum devices, including the superconducting quantum computer and the optical quantum computer. Different quantum error mitigation techniques based on different quantum devices will be included in the team's library. Moreover, the team will develop a tensor network approach to improve the training efficiency of the variational quantum circuits. In sum, this research focuses on investigating the architecture of the hybrid system and the optimization method in training. With their developed library, the team will apply it to detect various vehicle cyberattacks, improving driving security.]]></description>
      <pubDate>Fri, 09 Feb 2024 19:30:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2335054</guid>
    </item>
    <item>
      <title>Holistic digital twins for transportation infrastructure</title>
      <link>https://rip.trb.org/View/1945572</link>
      <description><![CDATA[Digital twin research has, to date, typically evolved from one of the many sub-domains that informs the topic, rather than as a consideration of the concept holistically [13]. In particular, efforts to delineate and optimize the information architecture of a twin are more limited, particularly when considering the particular case of transportation structures [14], [15]. As artificial intelligence begins to empower autonomous systems, identifying digital twin system architectures has become a critical need for the development of truly “smart” transportation systems. 
The objective of this research proposal is to identify best practices for the delineation of a digital twin information architecture, and then employ those practices in a prototype implementation, for the specific context of transportation structures. Achieving this will lead to advancements in the management and utility of information regarding transportation systems. The creation of a framework for the integration and management of heterogeneous information will allow for new methods of fusing that information together via AI [16], [17], while also supporting advanced human-infrastructure interactivity through technologies such as virtual and augmented reality. The emphasis will be on bridge structures, due to the PI’s familiarity with the domain, though the goal will be to create a generalizable framework applicable to a range of infrastructure systems.
]]></description>
      <pubDate>Thu, 28 Apr 2022 19:49:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/1945572</guid>
    </item>
    <item>
      <title>Strategies to Strengthen Data-Driven Decision Making</title>
      <link>https://rip.trb.org/View/1916002</link>
      <description><![CDATA[State DOTs are seeking to derive more decisions from data, improve real time performance management, and integrate advancements in data science. While data analytics, automation and machine learning are increasing, current data architectures are fragmented and costly, adding complexity and delay for information system development and management. This makes it difficult to maintain alignment with business needs. Business and enterprise architectures are used by some state departments of transportation as well as many other public and private organizations. Examples of these architectures include the Zachman Framework for Enterprise Architectures, The Open Group Architecture Framework (TOGAF), and the Federal Enterprise Architecture (FEA).

The goal of this project is to identify business architectures that support and optimize faster decisions, data relevance and usability across the organization, and current business needs and responsive to evolving needs.

The objective of this project is to explore business and enterprise architectures for their potential to improve the alignment of data with business needs and provide timely support for changes in business strategies. ]]></description>
      <pubDate>Thu, 17 Feb 2022 11:09:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1916002</guid>
    </item>
    <item>
      <title>Architecture for an Information System for Reporting and Sharing Truck Regulatory Requirements Data



</title>
      <link>https://rip.trb.org/View/1915998</link>
      <description><![CDATA[The goal of the trucking industry is to move goods safely, quickly, and profitably. State departments of transportation (DOTs) perform regulatory functions such as safety inspections, licensing, permitting, routing, and size and weight enforcement to ensure safe and lawful truck operation. DOTs routinely share regulatory information within their own boundaries and report certain information to the Federal Motor Carrier Safety Administration, but infrequently share information directly with neighboring states. Sharing information more often and more quickly with neighboring states could reduce unnecessary duplication of inspections and other regulatory functions, avoid unexpected delays caused by differences in regulations, and better inform truckers of temporary restrictions or allowances. Such information sharing would reduce state DOTs’ administrative costs and trucking industry’s operational costs and improve the reliability of freight transport. Sharing real-time data requires an information architecture, data standards, enabling technology, and a supporting organizational structure but no commonly accepted procedures for reporting such information exist now. There is a need to evaluate the need, feasibility, and benefits of real-time commercial vehicle data sharing among states; and develop an architecture for an information system that will support such data sharing among state DOTs and help accrue economic benefits while ensuring safety and compliance with regulations. OBJECTIVE: The objective of this research is to design and demonstrate an architecture for an information system for reporting and sharing data pertaining to truck regulatory requirements among state DOTs. For the purpose of this research, regulatory requirements encompass those pertaining to licensing, permitting, enforcement, and restrictions on vehicle movement.]]></description>
      <pubDate>Thu, 17 Feb 2022 11:03:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/1915998</guid>
    </item>
    <item>
      <title>Structural Health Monitoring Framework</title>
      <link>https://rip.trb.org/View/1537216</link>
      <description><![CDATA[Develop structural health monitoring (SHM) framework for CST vehicles, payloads and components. The framework encompasses sensors, electronics, signal processing and automatic decision making as integral part of methodologies enabling structural condition assessment, continuous monitoring, and system prognosis. It is envisioned that SHM framework will serve as a key component of a future spaceflight recorder ("black box") that reports vehicle's health information and could facilitate re-certification for the next flight. Building on SHM system knowledge and expertise obtained in 2 successful spaceflights, the research team will explore fundamental aspects of structural monitoring in space, adapt existing sensors to launch/space/reentry operation, develop compensation routines for unfavorable influences of space environment, infer signal processing schemes and automatic decision support for SHM methodologies suitable to space applications. Practical examples of SHM of space system (planned through NASA FOP or other flight opportunity) will be demonstrated and system architecture compatible with "black box" will be explored. Planned collaboration with commercial launch providers will allow to tune SHM to specific launch vehicles and/or payloads.  AST GOALS: This task is aimed at improving safety and affordability of commercial spaceflights. In this capacity it supports AST's mission to ensure protection of the public and property and to carry out safety responsibilities. Safety inspection is an AST core function and as such structural health monitoring task will directly support AST's mission.]]></description>
      <pubDate>Wed, 22 Aug 2018 13:03:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1537216</guid>
    </item>
    <item>
      <title>Harmonization Execution</title>
      <link>https://rip.trb.org/View/1500409</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Thu, 01 Feb 2018 14:09:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1500409</guid>
    </item>
    <item>
      <title>PPRC14 SPE 4.53: Validation of Greenhouse Gas Emissions from Pavement Deflection</title>
      <link>https://rip.trb.org/View/1441820</link>
      <description><![CDATA[The task will be amended to complete the funding of the field test validations.  The field tests are very sensitive and will require more accurate measurements.  The field validation will be the means to assess which of the proposed deflection models capture the physics properly.  Follow up to 13/14 project to review mechanistic algorithms from Massachusetts Institute of Technology (MIT) and other research centers to calculate viscoelastic energy dissipation from vehicle operation for different pavement types, climate regions and vehicle types.  Perform field validation of fuel economy differences, compare results with models, and then perform comprehensive assessment for state network implementation. Task objectives are as follows: (1) Develop user and system functional requirements; (2) Design and implementation of new system architecture; (3) Design and implementation of an ME compute engine; (4) Design and implementation of a web-based user interface (UI); (5). Perform system integration of CalME v3.0; (6) Perform testing; (7) Provide user support for CalME v2.0; and (8) Develop user and system documentation.]]></description>
      <pubDate>Wed, 04 Jan 2017 10:53:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1441820</guid>
    </item>
    <item>
      <title>Quality of Service and Service Availability in Mission Critical Service Oriented Architectures - An Analysis of the FAA NextGen SWIM Architecture</title>
      <link>https://rip.trb.org/View/1360972</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Wed, 15 Jul 2015 01:01:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1360972</guid>
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