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This Solicitation opportunity from Nevada was posted on January 23, 2024. The submission period has ended. Browse the details below for market research, or find similar active opportunities.

Artificial Intelligence Intersection Video Analytics

Closed
24-262J-1State & Local

Contract Overview

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NAICS: 541715
New
Federal
Atomically Precise Molecules (APM)
Solicitation # DARPA-PS-26-141
The Atomically Precise Molecules (APM) program, solicitation DARPA-PS-26-141, is a 36-month initiative by the DARPA Multi X Office seeking innovative synthesis techniques for programmable precision polymers (PPPs) and nonlinear optical (NLO) materials. The program aims to develop scalable materials with strictly controlled monomer sequences and compositions, culminating in a prototype NLO material integrated into a government-provided electro-optic modulator capable of achieving greater than 1THz modulation. The effort is structured in two phases, with Phase I lasting 12 months and focusing on synthesis capabilities. Research limited to inorganic materials or incremental improvements to existing state-of-the-art practices is specifically excluded. Awards will be issued as Other Transaction for Prototype Agreements, with payments processed through the Wide Area Workflow system based on a schedule of milestones. Proposals are due by December 18, 2026, via the Broad Agency Announcement Tool and must include technical, management, and pricing volumes. Eligibility is restricted to U.S. organizations, and all performers must comply with CMMC Level 2 self-certification and NIST SP 800-171 standards for safeguarding Controlled Unclassified Information. Evaluation will be based on technical approach, relevance to the DARPA mission, budget realism, and the capability to transition the technology to military or commercial sectors.
Defense Advanced Research Projects Agcy

POSTED

1 day ago

DEADLINE

in about 2 months

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The contract involves the deployment of an Artificial Intelligence Intersection Video Analytics System by the Regional Transportation Commission of Southern Nevada, funded through the USDOT FY 22 SMART Grant initiative. The primary goal is to enhance traffic management and safety by leveraging existing camera feeds from RTC FAST to collect and analyze traffic data at signalized intersections. The system aims to identify near-miss incidents involving pedestrians, bicyclists, vehicles, transit, and emergency responders, with a particular focus on red light running, traffic volumes, and overall intersection performance. The collected data and insights will be accessible via a web-based platform, enabling RTC to monitor incident rates and severity, thereby supporting safer and more efficient transportation operations. The solicitation, titled "Artificial Intelligence Intersection Video Analytics," is posted under solicitation number 24-262J-1, with a response deadline of February 13, 2024. The project falls under NAICS code 541715, indicating a focus on research and development in computer programming, consultancy, and related services. The contracting organization is the Nevada-based Regional Transportation Commission of Southern Nevada, with primary contact being David Gloria. The procurement process emphasizes compliance with cybersecurity and submission protocols, and the contract is expected to facilitate innovative traffic analytics solutions that can improve intersection safety and operational efficiency through advanced AI and machine learning technologies.

Documents

1

Bid Invitation.pdf

PDF

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Timeline

PhaseClosed
Posted

Solicitation

Response Deadline

Deadline has passed

Submission Closed

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Organization & Contact Information

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AgencyRegional Transportation Commission of Southern Nevada
Contacts1 person available
OfficeN/A

Full Description

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RTC desires to deploy an Intersection Advanced Analytics System using artificial intelligence (AI) to collect traffic data and provide insight into intersection operations and safety as part of the USDOT FY 22 SMART Grant: Safer, Smarter, Faster. The project intends to utilize existing camera feeds available from RTC FAST and utilize artificial intelligence and machine learning to gain insights into near miss incidents, involving pedestrians, bicyclists, and vehicles, particularly with transit and emergency vehicles, red light running occurrences, traffic volumes and performance of signalized intersections. Collected data and analytics should be made available through a web-based platform accessible by RTC and indicate rates and severity of incidents.

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