CCAT Funding Opportunity (Request for Proposal)

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Total Available Federal Funding: $2.1M
Issue Date: October 31, 2025
Proposal Due Date: January 6, 2026
For questions, please contact Henry Liu (CCAT Director) and Debby Bezzina (CCAT Managing Director)

Overview of CCAT

The University of Michigan at Ann Arbor, in partnership with Central State University, Purdue University, University of Akron, University of Illinois at Urbana-Champaign, and Washtenaw Community College, established the USDOT Region 5 University Transportation Center (UTC): Center for Connected and Automated Transportation (CCAT) in 2016 under the funding support from the FACT ACT. In 2023, CCAT was renewed under the support of the Bipartisan Infrastructure Law (BIL), with an expanded consortium adding Northwestern University, University of Minnesota, Twin Cities, and University of Wisconsin-Madison.

The statutory research priority area that CCAT addresses is Promoting Safety. Under BIL, CCAT is USDOT’s designated UTC “focusing its efforts in the field of comprehensive transportation safety, congestion, connected vehicles, connected infrastructure, and autonomous vehicles, including the cybersecurity implications of technologies relating to connected vehicles, connected infrastructure, and autonomous vehicles”.  

CCAT Vision:  Provide regional and national leadership in connected and automated transportation system research, education, and training.

CCAT Mission:  Significantly advance the evolution of the U.S. next-generation transportation systems with connected and automated vehicles and infrastructure. 

CCAT invites proposals from the community of researchers within the partner institutions. CCAT seeks both fundamental and applied research proposals that align with the CCAT mission. CCAT particularly values proposals with demonstrable outcomes or significant impacts on the field or state-of-the-practice. This year, CCAT is soliciting one-year projects that will have a tangible scope that will produce direct and noteworthy results.

Additionally, CCAT encourages researchers to utilize the Mcity 2.0, a remote-accessible mixed reality test facility designed for connected and automated vehicles and infrastructure. Mcity 2.0 is funded by the National Science Foundation and will provide funding for researchers to use the test facility. For information regarding the research use cases supported by Mcity and how to make the request to use the facility, please visit: https://mcity.umich.edu/what-we-do/mcity-test-facility/remote-access/.

Funding Opportunity Description

Connected and automated vehicle (CAV) technologies hold the potential to substantially improve traffic safety and mitigate traffic congestion. As CAV technologies progress towards integration into public roadway systems, there exist a variety of open questions and issues on technology development, policy and planning, and system design and operations that require answers and resolution.

To address these questions, CCAT has created working groups to focus on four research thrusts: (1) advancing safety through accelerated training and testing of CAV and vehicle-to-everything (V2X) deployment, (2) enhancing mobility by developing connected infrastructure and cooperative driving automation, (3) hardening the CAV ecosystem to reduce cybersecurity risks, and (4) leveraging CAV and infrastructure to enhance transportation accessibility.

The following research topics are identified by the CCAT Work Groups and the CCAT Technical Advisory Board (TAB). However, the following list of topics is not meant to be an exhaustive list. Instead, CCAT is actively seeking proposals that present innovative concepts capable of expediting the progress and implementation of connected and automated transportation systems, with the aim of advancing safety, improving mobility, strengthening cybersecurity, and promoting accessibility. Furthermore, CCAT highly encourages proposals that build upon the achievements of previously funded projects. For further information regarding CCAT and the ongoing research projects that are currently being funded, please visit ccat.umtri.umich.edu.

• Safe operation of autonomous vehicles:
  • Safe interaction with vulnerable road users
  • Verification and validation in production AVs with OTA update
  • Edge scenarios for AVs now and in the next 5 years
  • How can AVs and connected technology be integrated to enhance safety?
  • How do emergency responders best communicate with autonomous vehicles?
  • Elderly drivers and driving safety in the context of different levels of ADAS/AVs. Elderly drivers have difficulty understanding technology and how they perform. This lack of understanding might pose challenges in safe operation on ADAS, different levels of AVs like timely handover, etc.
• Connected Vehicle and Technologies:
  • Further development and use of the Ann Arbor Connected Environment
  • Uu and PC5 interoperability:
    • How to differentiate services based on application needs. How to best trust data (authentication, accuracy latency) between Uu and PC5 and Uu-to-Uu with different network operators that exchange standardized messages?
    • Handoff or transition between Uu and PC5. For example, when far from a work zone use V2N, but approaching the work zone transition to V2X.
      • Integration of V2N (network) and V2X (direct)
      • Distribution of latency
      • Modeling and simulation of the above
  • What is the best way to describe data quality such that it can be assessed for operations “suitability of purpose”? To address the challenge of ensuring reliable data, various plausibility checkers have been developed to identify faulty transmitter data. However, there is a lack of assessment regarding the identification and quantification of the faults that escape these checkers.
  • Using a smartwatch for cyclists, e-scooter riders, pedestrians and other vulnerable road users as a connected technology of vehicle interaction with VRUs for safe roadway operation.

• CAV readiness of roadway infrastructure. As connected and automated vehicles (CAVs) advance toward broader deployment, a growing challenge for state Departments of Transportation (DOTs) is assessing whether existing roadway infrastructure is perceptually compatible with CAV technologies. While most DOTs maintain detailed inventories of infrastructure elements—such as lane markings, traffic signals, and signage, there remains a critical gap in standardized methods to evaluate their detectability, interpretability, and robustness under real-world CAV operation. Moreover, few agencies employ field-based validation strategies, such as using AVs or perception rigs to traverse test corridors and assess infrastructure support for core driving tasks (e.g., lane keeping, signal recognition, infrastructure-to-vehicle (I2V) communication). CCAT has already completed some research in this area and should not be duplicated. Information and final reports can be found on the project page on the CCAT website.
• CAV-guided infrastructure control design. Traffic engineers design control infrastructure based on how the traffic flows. However, this will be disrupted as CAVs begin to dominate the traffic flow. However, it is unclear how to design infrastructure control that is flexible with respect to different CAV deployment scenarios. Understanding how to develop optimal infrastructure control with automation as an input is an open question
• Risk analysis and crash scene assessment based on vision-language models. Intelligent vehicles’ perception capabilities are rapidly developing. And although autonomous vehicles are capable of capturing rich multimodal data from their surroundings, existing research has primarily focused on vehicle-level decision-making and driving safety, leaving the systematic use of perception data for infrastructure evaluation and risk analysis underexplored. At the same time, vision-language models (VLMs) have demonstrated strong abilities in interpreting professional and complex textual information, yet their integration with AV perception data remains limited. Currently, there is a lack of standardized approaches that combine real-time environmental sensing with semantic interpretation to conduct comprehensive assessments of roadway infrastructure conditions, traffic incidents, and potential risks. This gap hinders the development of more fine-grained problem detection, risk classification, and evidence-based decision support for transportation agencies
• Integration of information management platforms at intersections to orchestrate first responders’ safety and mobility
• Data Governance and Enterprise Data management:
  • How might data governance frameworks be streamlined to accommodate the fast pace of digital transformation in the transportation domain?
  • What would an effective strategic vision for agency data governance needs look like that incorporates roadway digital infrastructure (RDI) and digital transformation considerations, and how might agencies move towards “vision oriented” data governance models?
  • How might IOOs accelerate their data governance/management practices to make their data more accessible, interoperable and timely?
• Incentivizing RDI deployment and commercialization:
  • Where do emerging RDI capabilities deliver the most cost effectiveness on a use-case basis?
  • How can we shorten the innovation-to-adoption timeline?
  • What are the optimal step-by-step deployment paths for V2X?
  • What RDI-related human and AI resources do we envision requiring in the 3-, 5- and 10-year timeframes to deliver desired transportation outcomes? What are the top 3 priority actions that we need to take to get these resources in place?
  • How can we best enable regional/corridor efforts to scale advancing RDI capabilities?

• Today, cybersecurity testing is quite labor intense. There is functional security testing against security requirements (verification) and pen testing (unstructured testing or validation). There is work done on utilizing AI to heavily improve the efficiency of these testing tasks. However, can AI be used to design test cases for the testing of sensor security and perception algorithms?
• DARPA’s AI Cyber Challenge (AIxCC) just ended. The challenge was for AI driven software to defend automatically against threats (challenges). The objective was to utilize AI to better protect US infrastructure. It is just a matter of time until AI will be used to attack vehicles and vehicle infrastructure, and hence it’s foreseeable that AI driven defenses need to be deployed as well. It would be interesting to analyze the outcome of the AIxCC and similar efforts and apply the results to the automotive and transportation domain
• How can models be adapted to other regions of the world? Can local training data (e.g., Chinese data, European data, US data) be combined into a dataset that then works in all original regions? Investigate whether this would be permissible with regulation (e.g., can data poisoning be avoided here). This is interesting to train self-driving vehicles or automation features without violating any privacy/data export laws
• The concept of cooperative driving automation (CDA) has been proposed for a few years. Yet, the mainstream of AV development only relies upon onboard sensors without considering external information. One major hurdle is the trustworthiness of external information sources, especially for safety critical applications. It lacks a fundamentally secured V2X network design from the physical layer to the application layer
• Post quantum crypto: (1) Apply generic research results to transportation and provide guidance for transportation; (2) Understand impact to transportation sector if there are quantum computers; and (3) Guidance around crypto agility and updates to post-quantum crypto
• How can we protect the image of a sensor (camera, radar, lidar) by cryptographic means, but the receiver only authenticates/decrypts the relevant portion of the image?
• How can we ensure end-to-end system security and reliability in a system of systems environment at scale?

• Automated vehicles have the potential to greatly improve accessibility in rural areas by providing reliable, on-demand transportation where traditional public transit is limited or nonexistent. Many rural communities face challenges such as long travel distances, limited driver availability, aging populations with restricted mobility, lack of fresh foods/vegetables and healthcare. Automated vehicles can bridge these gaps by offering affordable and continuous transportation for essential needs like medical appointments, grocery trips, and social activities. They can also bring healthy food and healthcare directly to these communities. Research needs can include what areas can benefit most from AVs in providing access in rural areas, what partnerships are most effective and how to achieve them, how to generate funding and long-term sustainability for such programs, etc
• The economics of providing accessibility-enhancing autonomous mobility services. Autonomous vehicles have the potential to improve mobility but the populations that stand to benefit the most (e.g., elderly, handicapped, ill, low income) also have limited means to bear the costs of emerging technology. What are the likely full production-level costs of autonomous transportation solutions and will a gap exist between cost and ability to pay? What price points should be targeted now during AV development to ensure technology is economically feasible for critical user populations? If government agencies wish to use AVs (e.g., the Veterans Administration using AVs to enhance veteran access to VA healthcare facilities), how will the cost and performance of AV-based solutions compare to traditional approaches?
• Researchers have developed survey measures of constrained and unsafe travel and unmet demand at the individual level. How these metrics relate to measures of accessibility have not yet been assessed. Neither has their relationship to measures of cost burden or actual movement as collected through GPS. This leaves a gap in understanding what metrics mean and which one should be used to measure the performance of the transportation system, including how AVs will impact that
• VR-based evaluation of accessibility in-vehicle and human-vehicle interface (HMI) designs for automated vehicles (AVs), focusing on boarding/securement, communication, interaction, and emergency protocols for riders with disabilities. The idea is to simulate AV ride experiences in immersive VR and test variant interface designs (visual, audio, haptic) with diverse users before physical prototypes
• Autonomous vehicles (AVs) lack validated, inclusive emergency protocols for riders with disabilities. Current AV pilots rarely test what happens when an AV stops mid-route, encounters system failures, or requires passenger evacuation. Riders with vision, hearing, cognitive, or mobility impairments may not understand or follow instructions delivered by standard systems. There is a gap in evaluating accessible, multimodal emergency communication and procedures for passengers with visual impairments
• The role of mobility wallets and resource transfer via apps/digital tools to increase accessibility and identifying the amount of money that should be offered via mobility wallets that would be effective and see an acceptable ROI

Proposal Submission Instructions

Create a single PDF file with the cover sheet and the required proposal elements and submit electronically to: Debby Bezzina (dbezzina@umich.edu)

Important Dates
– Proposal Due: January 6, 2026
– Target CCAT project launch date: June 1, 2026

Budget Limits
– Proposal with single Consortium Member – $150,000
– Proposal with multiple Consortium Institutions – up to $150,000 per consortium member, maximum $300,000

Required Proposal Elements:

1. Cover Sheet: Complete the proposal cover sheet (see attachment) and include it as the first page of the project proposal.
2. Project Abstract (Limited to 1 Page): Concise summary of the project.
3. Proposal Description (Limited to 5 Pages, additional materials are allowed as appendices)
   • Introduction
   • Technical Approach, including any challenges
   • Proposal Tasks
   • Schedule
4. Letter of commitment from industry or government principals for CCAT proposals.
5. Appendix A: Response to Evaluation Criteria – write no more than a paragraph on each of the following, pulling from your proposal description as necessary. If pulled from your proposal, indicate the page(s) that the reader can find additional details. Use the headings as written below (the part in bold). If the section is not applicable to your proposal, under the heading write n/a.
   • Technical Evaluation
     • Technical Innovation. Describe the key innovation and relevance to the CCAT Research Thrusts.
     • Technical Feasibility. Describe why the research is feasible.
     • State of the Art. Describe what background search was completed and an understanding of what it would take for the technology to be implemented.
   • Impact Evaluation
     • Results. Describe how this research will result in a deployment, demonstration, or other implementation; or how this research will influence standards development; or generate open-source software; or result in intellectual property; or a combination thereof.
     • Research Champion Involvement and matching funds. How will your champion be actively involved in the research? Does your champion have plans to implement the research results if the project is successful? Do you have any external funding sources or a plan to attract them? If so, please identify the source(s).
     • Collaboration among Consortium Institutions. Will you be working with any other CCAT organizations?
   • Educational Evaluation. What educational materials will be developed? Where will those educational materials be used? Will students be actively participating in your research?
   • Budget Evaluation
     • Matching Funds Source. Is any of your funding from industry, state or local government agencies, and/or foundations?
     • Matching Funds Usage. Please specify how the funds will be used for your research. Funds may be in-kind but must be shown in your budget.
6. Appendix B: Itemized Budget and budget justification:
   • Budget Justification. Describe level of effort to perform the tasks in the project description.
   • Itemized budget:
     • Faculty and Staff Salaries, with fringe benefits broken out
     • Graduate Student Research Assistant (GSRA) Salaries, with fringe benefits broken out
     • GSRA Tuition
     • Supplies/Materials
     • Travel
     • Equipment
     • Other
     • Total Direct Cost Amount
     • Indirect Cost Amount
     • Total amount requested
     • Cost share
     • Total project cost
7. Appendix C: Resumes. Short bios of the PIs: no more than two pages for each primary researcher. Bios should include pertinent links including LinkedIn, Twitter, ResearchGate, Google Scholar, personal website, etc.