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2026-09-06
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Cooperative Driving Automation (CDA) Small-Scale Industry Prototype

693JJ3-26-BAA-0005 · FEDERAL HIGHWAY ADMINISTRATION · 693JJ3 ACQUISITION AND GRANTS MGT

transportation infrastructure ai data science cybersecurity emergency disaster resilience

Closes
2026-09-13 · 7 d
Award ceiling
Award floor
Program funding
Expected awards
Cost sharing
No
Posted
2026-07-30
Instrument
Characterization · gpt-5.4-mini · 2026-08-13

This solicitation funds industry-led small-scale cooperative driving automation prototypes at TRL 6-7, including controlled testbed validation and limited real-world public-road demonstration for safety and mobility use cases.

Funds
applied research
University
ineligible
social behavioral
minor
physical sciences
minor
engineering
central
computational data
substantial

⚑ Federal contract acquisition, not a grant. · Industry-led prototype effort; university would not be a direct applicant. · No set-aside stated.

Unit fits — one characterization, each unit's own rules

Tom Love Innovation Hub 45 partial funds applied research; prototyping/demonstration stage; deep-tech content
IPPRA 15 none university cannot apply directly (ineligible)
Physical Sciences & Engineering (demo) 15 none university cannot apply directly (ineligible)

Description

BACKGROUND: USDOT envisions a future transportation ecosystem where CDA complements both human-driven and automated vehicles to improve roadway safety, mobility, and operational efficiency. While standalone vehicle automation has advanced, it continues to struggle with complex edge cases such as occlusion, complex merging, or identifying pedestrians hidden behind buildings at corners, and vehicle sensors alone cannot adequately resolve these issues. Prior USDOT-supported research projects have successfully demonstrated the technical proof-of-concept at technology readiness levels (TRL) of 3-5 for CDA applications in laboratory and closed environments. However, most new vehicles are not equipped with CDA capabilities and Original Equipment Manufacturers (OEMs) remain reluctant to integrate these technologies into production lines due to a lack of consumer awareness and unclear regulatory, policy, and institutional frameworks. This acquisition seeks to build on foundational CDA research by bridging the gap between research concepts and commercial viability. There is a critical need to develop and test pre-production, industry-led prototypes at a TRL of 6-7, to validate that the data shared between vehicles and roadside infrastructure is accurate, secure, and reliable enough to support functional safety. By demonstrating CDA technologies in operational conditions, researchers will gain a better understanding of cooperative systems and if they are more valuable in terms of improving roadway safety, than standalone automation or vehicle-to-everything (V2X) technologies alone. PURPOSE: The purpose of this requirement is to demonstrate industry-led CDA prototypes in controlled and real-world conditions in order to generate data-driven insights that will support the development of a national strategy to deploy CDA across rural, suburban, and urban roadway systems. This effort prioritizes industry-ready CDA technology for use cases with clear safety and/or mobility benefit potential, specifically focusing on: (1) Work Zone Management, (2) Traffic Incident Management, (3) Cooperative Perception, and/or (4) Emergency/Transit Vehicle Awareness. Contractors will first validate the prototype's interoperability, functional safety, and cybersecurity in a relevant, controlled testbed environment. Following successful controlled testing, the effort will progress into a small scale real-world demonstration on public roads with mixed traffic to validate operational readiness and generate data-driven insights.

Eligibility

Applicant restrictions (federal contract). Set-aside: No Set aside used. Notice type: Solicitation. Organization: TRANSPORTATION, DEPARTMENT OF / FEDERAL HIGHWAY ADMINISTRATION / 693JJ3 ACQUISITION AND GRANTS MGT.

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ONE LLM CALL (~1¢) · CACHED · REQUIRES STAFF KEY

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