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Connected Vehicles perception module

Problems addressed

Urban blind spots and streaming raw video to distant cloud servers create high latency, high bandwidth costs, and severe safety risks for pedestrians and cyclists.

Unique Value Proposition

Bridges AI, 5G MEC edge nodes, and roadside sensors to deliver real-time hazard warnings with millisecond latency reduction while keeping raw video local.

Validated UC & Key Performance Benchmark

(Where was it tested, and what exact performance improvement or resource reduction did it achieve?)

Tested in: Validated in real urban traffic at the Modena Automotive Smart Area (MASA) Living Lab using TIM’s commercial 5G MEC infrastructure.

Hard metrics:

  • ≥90% VRU detection accuracy
  • Millisecond latency reduction
  • 20+ deployed smart camera nodes to ensure 100% data sovereignty due to local raw video processing

Target Stakeholder Segments

(Who adopts/Hands-on Users)

Smart City Traffic Engineers, Autonomous Driving Systems Developers, Telecom Edge Engineers, Vehicle Manufacturers

Key Industry / Vertical Markets

(Where it is deployed)

Connected & Automated Mobility, Telecommunications (5G MEC Operators), Automotive OEMs

License Model & IP and Commercial Strategy

(Open Source License)

Open Source License:

Commercial Streams: Scenario AI licensing, integration, & support; Specialised system integration services and technical support; Premium 5G Edge Infrastructure & Connectivity-as-a-Service packages

Proprietary Strategy: Software Copyright & Joint IPR Agreement

Availability

(Code Repository link)

Open Source License:

Commercial Streams: Scenario AI licensing, integration, & support; Specialised system integration services and technical support; Premium 5G Edge Infrastructure & Connectivity-as-a-Service packages

Proprietary Strategy: Software Copyright & Joint IPR Agreement

Estimated Time to Market

>3 years post-project
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