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    <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" />
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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> Simulating Accessibility from CAVs and ICTs (SACI)</title>
      <link>https://rip.trb.org/View/2680126</link>
      <description><![CDATA[Simulating Accessibility from CAVs and ICTs (SACI) develops a simulation tool that helps transportation agencies understand and plan for the transformative impacts of connected and automated vehicles (CAVs) and information and communication technologies (ICTs) on travel behavior and network demand. As CAVs and ICTs reshape how people choose destinations and routes, new models are needed to predict future demand and usage. The project develops a framework that captures cognitive, perceptual, and behavioral effects of CAVs and ICTs, implements it in an agent-based model using SILO, MITO, and MATSim simulation components, and packages the result as a software tool for use by state, regional, and local DOTs. The model uses multimodal transportation network data from the DC, Maryland, and Virginia region to assess how CAV-ICT deployment affects travel patterns and land use across the region.]]></description>
      <pubDate>Wed, 11 Mar 2026 15:25:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2680126</guid>
    </item>
    <item>
      <title>Context Aware Optimal Information Selection for Reliable, Resilient, Secure, and Efficient
Cooperative Perception</title>
      <link>https://rip.trb.org/View/2676000</link>
      <description><![CDATA[Cooperative perception significantly enhances a vehicle's local field of view by leveraging shared information from nearby vehicles, thus improving overall situational awareness. However, in densely populated environments, cooperative perception can place substantial strain on both communication band-width and computational resources. Such scenarios often result in excessive redundant information, where multiple vehicles repeatedly report the same objects, provide data at unnecessarily high frequencies, or share information irrelevant to the ego vehicle's current context. These issues cumulatively increase computational overhead prior to data fusion and lead to prolonged decision-making times.
Therefore, an effective filtering mechanism is necessary to selectively retain only the most informative objects. Higuchi et al. proposed a value anticipation-based Vehicle-to-Vehicle (V2V) communication approach. In their method, the sender evaluates the potential informational value to receivers and, based on real-time network conditions, either defers or cancels transmissions. This ensures that primarily essential information is disseminated to neighboring vehicles. In another related study, Zhou et al. introduced the Augmented Informative Cooperative Perception (AICP) algorithm, which incorporates both a routing mechanism and message filtering at the receiver side. Their algorithm utilizes an informative-ness measure to assess and select messages, optimizing resource use while ensuring relevant data is received.

While redundant messaging is typically seen as a problem due to its computational demands, it can also provide significant benefits in enhancing security within V2X communications. Specifically, redundancy can enhance detection of malicious behavior through corroborative data from trustworthy vehicles, thereby improving the security of V2X communications. Lie et al. proposed Misbehavior Detection for Collective Perception Services in Vehicular Communications (MISO-V), which leverages redundancy from received V2X messages to validate incoming perception information. Upon verifying a new message against redundant data, the receiver updates the sender’s trust score based on whether the information is classified as benign or potentially malicious. This updated trust score subsequently guides down-stream tasks in determining whether to integrate or discard information provided by that sender.

Balancing redundancy is thus crucial - maintaining an optimal level of redundancy can simultaneously enhance security and sustain computational efficiency. A suitable approach involves dynamically adjusting redundancy based on multiple factors, including source reliability (assessed via trust mechanisms), the planned route of the ego vehicle, prevailing network conditions, and the Age of Information (AoI). This strategy ensures that cooperative perception remains robust, secure, and scalable, supporting accurate and timely decision-making within cooperative vehicle networks.

The aim is to establish a balance between purposeful and efficient redundancy and safety against potential attack scenarios, optimizing the use of communicated data and the reliability of data fusion necessary for downstream tasks such as planning and control. The research team will explore information redundancy, perception inconsistencies, context aware fusion, spoofing and other attack scenarios, and the detection of attack patterns and will employ optimization strategies and reinforcement learning techniques. The focus will include intersection scenarios with varying traffic densities and connectivity levels. In addition to using the VeReMi dataset, the team will explore extensions to more realistic collaborative perception message attach scenarios for evaluation and validation.
]]></description>
      <pubDate>Mon, 02 Mar 2026 19:08:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676000</guid>
    </item>
    <item>
      <title>Mixed Virtual Reality as an Aid in Advancing the Reliability and Robustness of Connected and Automated Vehicle Applications</title>
      <link>https://rip.trb.org/View/2675998</link>
      <description><![CDATA[The rigorous evaluation of safety critical Connected and Automated Vehicle (CAV) scenarios, faces some significant hurdles. Physical testing of scenarios (including edge-cases) presents risk and cost challenges as it is inherently dangerous, cost-prohibitive, and often non-reproducible. Additionally, purely virtual simulation lacks the real-world complexity of communication latency, interference, sensor noise profiles, and realistic representation of physical vehicle dynamics. To address this, the research team proposes using Mixed Reality (MR) co-simulation on a closed-course test track. This powerful alternative merges the real-world fidelity of a physical test platform (live sensor data, vehicle kinematics, real wireless communication channels) with the reproducible complexity of a virtual environment. This enables the safe and rigorous testing of otherwise impractical edge cases. The MR testbed facilitates comprehensive evaluation, addressing critical challenges for example: (1) Robustness and Reliability: It allows for precise injection of sensor degradation faults and failures and enables V2X reliability stress-testing in real-world communication and interference. (2) Cybersecurity and PNT Resilience: The platform safely simulates False Data Injection (FDI) and Denial of Service (DoS) attacks into the V2X communication channel, testing the Vehicle Under Test's Intrusion Detection Systems. Furthermore, it assesses system reliability when Position, Navigation, and Timing (PNT) data is compromised (e.g., via GNSS spoofing), evaluating the system's ability to use V2X data for positioning correction or safe mode transition. This framework leverages the validated utility of Hardware-in-the-Loop (HiL) platforms to rigorously evaluate the real-time performance and resilience of V2X protocols and sensor data fusion architectures on embedded edge computers. The project will leverage the existing highly-instrumented vehicle platform previously developed through the U.S. DOE ARPA-E NEXTCAR Program, which will serve as the Vehicle Under Test (VUT). Collaboration with TRC will be leveraged to facilitate the setup and validation of the MR testbed.]]></description>
      <pubDate>Mon, 02 Mar 2026 18:57:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675998</guid>
    </item>
    <item>
      <title>Connected Vehicle Data</title>
      <link>https://rip.trb.org/View/2640696</link>
      <description><![CDATA[The Compass IoT company is performing a pilot project with the Missouri Department of Transportation (MoDOT) to provide data, both historical and over a four-month period, for the research team to build a proof of concept for the member states in the Original Equipment Manufacturers (OEM) Pooled Fund. This will allow the research team to show the member states the benefits that can be realized with this information. The data will be focused on work zone information, near miss data, and winter weather events.]]></description>
      <pubDate>Tue, 16 Dec 2025 09:56:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640696</guid>
    </item>
    <item>
      <title>Evaluating V2X Network Performance and Enhancing Safety and Security in Sensor Data Sharing for Connected and Automated Driving
</title>
      <link>https://rip.trb.org/View/2625308</link>
      <description><![CDATA[This project will investigate the sensor data sharing mechanism with C-V2X and networked vehicle-to-everything (V2X) communication technology in terms of safety, cybersecurity, and network performance with current bandwidth allocations. The research team will (1) develop a comprehensive evaluation framework of the V2X network performance (e.g., latency, throughput) under real – world complexities; (2) develop a data fusion model that fuses Sensor Data Sharing Messages (SDSMs) from multiple sources considering uncertainties in real-world V2X communication networks, errors in sensor-based object detection; and (3) develop a misbehavior detection model that can detect anomaly in SDSMs and evaluate the trustworthiness of the message within a short time.]]></description>
      <pubDate>Thu, 13 Nov 2025 15:28:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625308</guid>
    </item>
    <item>
      <title>A Low-cost Roadside Device System for Cooperative Automated Driving
Phase 2: Work Zone Safety Applications</title>
      <link>https://rip.trb.org/View/2625304</link>
      <description><![CDATA[Despite the significant progress in automated driving, technical challenges still exist, especially for complex Operational Design Domains (ODDs). A low-cost roadside device system, the Connected Reference Marker (CRM) System, was developed to facilitate connected and automated vehicle (CAV) localization. A project was funded by Center for Connected and Automated Transportation (CCAT) FY2024 to build a prototype system and evaluate its performance. The initial results show that the CRM System is capable of maintaining low positional errors and, therefore, has the potential to be a reliable solution for vehicle localization for cooperative driving automation (CDA). In this project phase, the research team aims to develop a deployable work zone safety system built upon the prototype CRM system from the previous project phase. Specifically, the work zone safety system can track and predict the trajectories of individual vehicles, estimate the vehicle’s collision risk, and send customized warning messages if the risk is elevated. This work zone safety system will also incorporate modules from the CARMA CDA platform to ensure system interoperability.]]></description>
      <pubDate>Thu, 13 Nov 2025 14:51:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625304</guid>
    </item>
    <item>
      <title>Urban Network Speed Optimization for Connected Automated Vehicles: Development and Testing</title>
      <link>https://rip.trb.org/View/2606410</link>
      <description><![CDATA[This research develops and evaluates optimal speed control strategies for Connected and Automated Vehicles (CAVs) at the network level, addressing critical gaps in existing research by incorporating multiple powertrain technologies including internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and hydrogen fuel cell vehicles (HFCVs). The study addresses real-world challenges such as communication delays, data transmission errors, and vehicle actuation complexities that are often overlooked in idealized research conditions. Using the INTEGRATION microscopic traffic simulation software, the research will implement advanced communication modules for vehicle-to-vehicle and vehicle-to-infrastructure interactions alongside vehicle speed control modules. The methodology involves formulating speed trajectory optimization as a constrained problem incorporating vehicle dynamics, fuel consumption models for different powertrains, and signal phase and timing data. Dynamic programming methods including A-star search algorithms will ensure real-time computational efficiency. The research includes extensive testing across varied traffic networks with different congestion levels and CAV market penetration rates, culminating in a scalable framework for generalizing results to large-scale networks including the entire U.S. roadway system through collaboration with Saudi Aramco.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:21:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606410</guid>
    </item>
    <item>
      <title>Roadmap for Innovative Application of GDOT's Digital Information Assets in Support of Developing the Digital Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2596466</link>
      <description><![CDATA[
This project proposes the development of a comprehensive roadmap for digital infrastructure in transportation which will provide the necessary guidance and insights to support the implementation of digital technologies and drive innovation in the transportation sector. ]]></description>
      <pubDate>Fri, 05 Sep 2025 13:03:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2596466</guid>
    </item>
    <item>
      <title>CyberTrans-AI Development of Transportation Cybersecurity Certificate Program for Transportation</title>
      <link>https://rip.trb.org/View/2559305</link>
      <description><![CDATA[Transportation systems have evolved in the last decades and the modern system heavily relies on digital technologies from traffic signals to communication portals. Ensuring the security of these systems is imperative to safeguard public safety, protect sensitive data, and maintain the smooth operation of transportation services. The failure to protect the security of transportation networks could lead to disruptions in traffic flow, potential crashes, and even threats to national security. The purpose of this project is to develop a certificate program in transportation cybersecurity for practitioners with the necessary skills and knowledge to effectively protect transportation systems from cyber threats. There are five major objectives listed as follows: (1) Understanding Cybersecurity Fundamentals: the proposed certificated program is to provide participants with a fundamental understanding of cybersecurity concepts relevant to transportation system engineering. (2) Knowing Industry-Practice Knowledges: the proposed program is to offer specialized cybersecurity issues on intelligent transportation system (ITS), such as vehicle-to-vehicle (V2V) communications, vehicle-to-infrastructure (V2I) communications, to ensure and protect the transportation infrastructure and data. (3) Conducting Risk Assessment and Management: the proposed certificated program is to train practitioners to identify and assess cybersecurity risks within transportation systems and develop risk mitigation strategies to the unique characteristics of transportation infrastructure. (4) Increasing Security Awareness and Training: the proposed certificated program is to promote a culture of cybersecurity awareness among transportation practitioners, to identify potential threats, and thus to follow best practices to mitigate risks. (5) Providing Continuous Professional Development: the proposed certificated program is to support ongoing education and professional development for transportation practitioners in cybersecurity, providing opportunities for further learning, skill enhancement, and staying abreast of emerging threats and technologies.

By achieving these objectives, a transportation cybersecurity certificate program can help build a workforce of knowledgeable and skilled practitioners capable of effectively safeguarding transportation infrastructure and ensuring the safety, security, and reliability of transportation systems.]]></description>
      <pubDate>Thu, 29 May 2025 21:37:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2559305</guid>
    </item>
    <item>
      <title>Advancing Cyber Resilience of Transportation System Management and Operation Programs </title>
      <link>https://rip.trb.org/View/2558388</link>
      <description><![CDATA[Transportation systems and technology across the United States are becoming increasingly interconnected, spanning local, state, and national borders. This integration supports safe and efficient transportation networks but introduces cybersecurity challenges. Many transportation system management and operation (TSMO) strategies rely on real-time data sharing, integrated traffic management centers (TMC), and interoperable technologies, all of which create new threat vectors for cyber incidents. The potential for severe injuries and fatalities, data loss, service disruptions, and other failures necessitate a more resilient approach to cybersecurity, shifting the focus from merely preventing attacks to ensuring the ability to recover and maintain essential functions during an incident. Research is needed to develop coordinated, cross-jurisdictional frameworks and methodologies to assess, manage, and enhance cyber resilience within integrated transportation systems. 

OBJECTIVE: The objective of this research is to develop a guide for incorporating cyber resiliency into state and local TSMO programs. At a minimum, the guide will address: 
(1) Build the foundation for future TSMO projects involving system integrations; (2) Evaluate and enhance cyber resilience in TSMO programs; (3) Identify cross-jurisdictional TSMO program stakeholders; (4) Improve intra-agency and interagency coordination;
(5) Reduce impacts in the event of cyber incidents; (6) Establish clear responsibilities and recovery processes; and (7) Support risk management through strategic planning. 

Accomplishment of the objective will require at least the following tasks.
TASKS: Task descriptions are intended to provide a framework for conducting the research. The National Cooperative Highway Research Program (NCHRP) is seeking the insights of proposers on how best to achieve the research objective. Proposers are expected to describe research plans that can realistically be accomplished within the constraints of available funds and subaward time. Proposals must present the proposers' current thinking in sufficient detail to demonstrate their understanding of the issues and the soundness of their approach to meeting the research objective.

PHASE I: (Task 1) Perform a literature review on relevant resources, frameworks, generally accepted industry standards and practices, and reports published by U.S. Department of Transportation (USDOT), National Institute of Standards and Technology (NIST), NCHRP, American Association of State Highway and Transportation Officials (AASHTO), Institute of Transportation Engineers (ITE), National Transportation Communications for Intelligent Transportation System Protocol (NTCIP), National Electrical Manufacturers Association (NEMA), etc. Conduct a national cross-jurisdictional scan (surveys and interviews with the majority of state DOTs and at least one representative municipality from each covered state with TSMO system integration needs) to cover the following topics: (1) Multimodal cybersecurity risks for TSMO projects and programs, including those related to data sharing, interoperability, connected infrastructure, cyber-physical systems (CPS) and multimodal operations. This assessment shall include the potential for failures and data loss across jurisdictional boundaries and transportation modes; (2) Reference materials to integrate cyber resilience and cyber security checkpoints into the systems engineering analysis process and support security by design for pre-procurement, procurement, deployment, implementation, system integration, maintenance and operation, intra-agency and interagency collaboration, and workforce development; (3) Possible threat vectors and mechanisms that would impact TSMO system availability; (4) Methods to map the flow of data, whether it is from a roadside device, such as an automatic traffic recorder, dynamic message sign, or traffic signal controller box, referencing the intelligent transportation system (ITS) system architecture, to detect anomalies in the data, and to devote immediate attention. (5) Near-term risks evolved by emerging technologies, including but not limited to vehicle-to-everything (V2X), digital twins, and artificial intelligence (AI); (6) Tools and noteworthy practices for coordinating cyber resiliency efforts and modes that focus on incident response incorporating identify, protect, detect, respond and recover; (7) Example narratives about how cyber resiliency/security must be embedded into the culture of infrastructure owner operators (IOOs) and across partner agencies; (8) Methods to incorporate ITS cybersecurity training into workforce development for TSMO staff and embedded information technology (IT) supporting staff; and (9) Defined roles and responsibilities for ITS network communications development and support between TSMO staff and IT supporting staff. Conduct gap analysis to identify synergy and conflicts of these topics, and areas for improvement.

(Task 2) Prepare an annotated outline that will serve as the basis for guide development in Task 4. This annotated outline is intended to provide the context for the subject matter in the guide, which will include key technical topics and associated issues, major concepts, current trends, state of the practice, illustrative case studies, and recommendations. The annotated outline shall clearly describe: (a) intended structure of the guide; (b) key topics and supporting issues to be presented in each chapter, using the topics in Task 1 as starting point; and (c) any other items to be included in the guide (e.g., appendix, figures, tables). (Task 3) Prepare Interim Report No. 1 documenting the findings of Tasks 1 and 2 and provide an updated work plan for the remainder of the research no later than 8 months after the subaward is awarded. The updated work plan must describe the methodology and rationale for the work proposed for Phase II. Note: Following a 1-month review of Interim Report No. 1 by the NCHRP panel, the research team will be required to meet with the NCHRP project panel via a virtual meeting to discuss the interim report. Work on Phase II of the project will not begin until authorized by the NCHRP. 

PHASE II: (Task 4) Execute the work plan according to the approved Interim Report No. 1. (Task 5) Prepare Interim Report No. 2 that includes a draft guide based on the final annotated outline and an updated work plan for the remainder of the research no later than 6 months before the subaward end date. The updated plan must describe the process and rationale for the work proposed for Phase III. Note: Following a 1-month review of Interim Report No. 2 by the NCHRP, the research team will be required to meet in person in Washington, DC with the NCHRP project panel to discuss the interim report. Work on Phase III of the project will not begin until authorized by the NCHRP. 

PHASE III: (Task 6) Present the research findings to appropriate technical committees of AASHTO for comments and any proposed revisions. Note: The research team should anticipate making two virtual presentations during the period of performance to appropriate technical committees at AASHTO meetings. Revise the draft guide after consideration of review comments. (Task 7) Prepare the final deliverables, including: (1) Guide. (2) Conduct of research report that documents the entire research effort. (3) Two-pager flyer of outreach materials to increase practitioner awareness and understanding of the guide. (4) Presentation with speaker notes summarizing the research results. The presentation shall specify the research purpose, objective, issues addressed, research product developed, and benefits identified in the guide. (5) Stand-alone technical memorandum titled “Implementation of Research Findings and Products.” 

Note: Following receipt of the draft final deliverables, the remaining 3 months shall be for NCHRP review and comment and for research agency preparation of the final deliverables. 
]]></description>
      <pubDate>Wed, 28 May 2025 13:35:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558388</guid>
    </item>
    <item>
      <title>RES2023-29: Connect and Automated Vehicle (CA) Readiness Plan</title>
      <link>https://rip.trb.org/View/2537312</link>
      <description><![CDATA[The Tennessee Department of Transportation (TDOT) desires an action plan for the implementation, operation, and maintenance of Connect and Automated Vehicles (CAV) technologies and use cases at traffic signals throughout Tennessee. The CAV Readiness Plan will impact how municipalities in Tennessee implement, operate, and maintain traffic signals. This research will build on previous efforts with a focus on research that can lead to actionable items.

TDOT has deployed 132 DSCR units along SR 1 and another 30 units along I-24 within the Smart Corridor limits. TDOT vision is to develop an action plan describing the implementation, operation, and maintenance of CAV technologies and use cases at traffic signals throughout the state. 

The CAV Action Plan should also account for evaluating switching from DSRC to C-V2X and other anticipated industry changes as that will impact how municipalities implement, operate, and maintain their traffic signals. This project will build on previous I-24 Smart Corridor studies and condition assessments with a focus on research that can lead to actionable items.]]></description>
      <pubDate>Mon, 14 Apr 2025 15:52:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2537312</guid>
    </item>
    <item>
      <title>Experimental Evaluations and Analysis of the Impacts of Denial-of-Service (DoS) Cyber Attacks on the Performance of Connected and Automated Vehicles (CAVs) </title>
      <link>https://rip.trb.org/View/2531079</link>
      <description><![CDATA[The project will conduct experimental studies to evaluate and analyze the impacts of Denial-of-Service (DoS) cyber attacks on the performance of Connected and Automated Vehicles (CAVs) for a deeper understanding of cyber security of CAVs to safeguard future intelligent transportation systems.]]></description>
      <pubDate>Mon, 31 Mar 2025 17:00:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2531079</guid>
    </item>
    <item>
      <title>Towards Effective and Realistic Security Testing for Real-World HATS Autonomy Stack</title>
      <link>https://rip.trb.org/View/2458970</link>
      <description><![CDATA["Problem statement and objectives:
The promises of highly automated transportation systems (HATS) are clear and compelling, but HATS will fail to gain the public’s trust if they are seen as uniquely vulnerable to cyberattacks. Recent works have discovered various possible security threats against HATS that can lead to severe consequences such as vehicle collisions and traffic rule violations. Thus, it is imperative to design effective and realistic security testing methods for real-world HATS, especially for those that are safety-critical, in or close to production, and also relatively new (e.g, vehicle autonomy), so that their cybersecurity problems and the associated risks can be proactively identified, understood, and addressed/regulated before wide deployment.
Scope:
In our last year’s project, we performed the first large-scale commercial HATS security testing, against the popular Traffic Sign Recognition (TSR) features in commercial vehicles today. We performed physical-sce-nario testings and in-depth analysis with real consumer vehicles from top-selling brands, which not only confirmed the existence of real-world security risks against commercial HATS today, but also uncovered various new scientific gaps. In this new 1-year period, we will build upon last year’s efforts to perform (T1) new methodology designs to address the discovered scientific gaps, especially on the generalizability of the HATS autonomy stack security threats to better understand their real-world risks; and (T2) effective and realistic simulation-based security testing method designs for real-world HATS, as the physical setup-based testing methods we used last year are fundamentally limited in its real-world scalability.
Specifcally, for this new 1-year period we plan to focus such new testing methodology designs for two highly-realistic HATS security threats: 2D image spoofing attack (using 2D images of road objects, e.g., cars/pedestrians, to spoof road objects and thus trigger unsafe/undesired driving), and adversarial sce-nario attack (using malicious driving maneuvers to trigger unsafe/undesired driving). Both threats have high realism in real world given the low attack cost and expertise requirements for the former (just need to print a normal photo), and the low legal liability risks for the latter (no need to alter legitimate road signs/objects). However, for the former, no simulation-based testing method exists today, and for the lat-ter, existing simulation-based testing methods lack scenario awareness (e.g., still optimizing for causing front collision when no obstacles are in the front), which fundamentally limits its testing effectiveness."

"Methods:
For task T1, building upon our project last year, we will focus on generalizability research on security threats against real-world TSR systems. Specifically, we plan to explore leveraging the latest generative-AI methods (e.g., diffusion models) to generate more generally effective AI attacks aross different commer-cial TSR; such a generative AI-based approach has recently been found capable of improving the transfer-ability of AI attacks in general, and we will be the first to explore this for HATS. For task T2, we will be the first to perform a formal definition of 2D image spoofing attack in HATS settings considering HATS-specific attack goals and constraints (e.g., attack image type/size/location based on driving scenario/vehicle dy-namics), and then design and develop the simulation-based testing method accordingly. For adversarial scenario attack, we will first reproduce latest testing method, and then design new scenario-aware testing methods, e.g., by designing new methods to systematically discover scenario-specific attack opportuni-ties and dynamically adjust attack objective functions.
This project involves both theoretical developments (new methodology designs above) and experimental demonstrations (detailed next). The planned simulations/experiments include but are not limited to: (1) Continued security testing efforts for real-world HATS systems/products; (2) Experimental evaluation of the new testing methods above against real-world HATS systems/products in both physical world and in-dustry-grade simulation environments. CARMEN+ center-internal collaborations will be actively pursued."]]></description>
      <pubDate>Thu, 21 Nov 2024 17:40:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458970</guid>
    </item>
    <item>
      <title>Ku-Band Array Antenna for Using LEO Satellite Signals in PNT</title>
      <link>https://rip.trb.org/View/2458988</link>
      <description><![CDATA[This project explores the use of signals from OneWeb and Starlink LEO satellite networks as an alternative to traditional Global Positioning System/Global Navigation Satellite System (GPS/GNSS) for positioning and navigation, aiming to overcome limitations like satellite visibility and signal strength. The CARMEN+ team at The Ohio State University, led by Dr. Zak Kassas, achieved 7.7-meter position accuracy using Starlink signals with commercial off-the-shelf horn antennas. The research seeks to design, fabricate, and test a low-profile Ku-band antenna array with 4-6 elements, focusing on optimizing beam-forming performance to ensure uninterrupted and accurate positioning. The project will use advanced digital beam-forming techniques to enhance satellite tracking and signal strength, ultimately aiming to improve positioning accuracy and robustness against interferences. Expected outcomes include a prototype antenna design, validated performance measurements, and potential new intellectual property.]]></description>
      <pubDate>Thu, 21 Nov 2024 17:32:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458988</guid>
    </item>
    <item>
      <title>Joint Sensing and Communications and Timing-based Attacks for HATS</title>
      <link>https://rip.trb.org/View/2458996</link>
      <description><![CDATA[This initial project will seek to understand the formulation for timing security in highly automated transportation systems (HATS). Position and Navigation security, both from the perspective of attack modeling and defense mechanism are farther along in the research community than Timing security in HATS. The research team's goal is to tackle the grand objective of closing this gap. Specifically, the team will consider timing attacks that can render state estimation and communication in HATS that can potentially result in safety incidents in addition to loss of reliability and data privacy. The research team will take a foundational approach to this problem, considering outside and insider attacks towards capturing and manipulating timing information in state estimation and communication. This will provide guidance on communication and coding policies that then will be applied to use cases relevant in CARMEN+. Brief specifics follow. Timing information is crucial in HATs, in a variety of enabling technologies, methodologies and use cases including in localization and situation awareness in cooperative autonomous driving. This project will define the cybersecurity attack surface created by the crucial timing information. The identification and classification of attacks are comprehensive, though we shall focus safety in connected vehicular networks. In this project, research team will investigate the use of timing signals and timing information to enable safe connected highly automated vehicular networks. These networks have to utilize timing information in wide ranging scales from incorporating situational awareness into autonomous/automated driving actions (eg for accident prevention) (micro or millisecond time scale) to cooperative routing (seconds or more). the research team will tabulate the new attack surfaces that can results in these scenarios due to: external unintended factors (eg interference on communication signals); malicious external attackers (eg jamming timing signals, poisoning timing signals); internal (eg a vehicle in a platoon) attacks.
]]></description>
      <pubDate>Thu, 21 Nov 2024 16:27:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458996</guid>
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