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Traffic

Traffic Incident and Hazard

Using VLM crash understanding and road-hazard detection, existing intersection, corridor and bus-mounted cameras are upgraded into a 24x7 proactive incident-sensing system. It recognises crashes in real time (type, vehicles involved, injuries, vehicle damage, whether police, ambulance or fire are on scene), road hazards (debris, stalled vehicles, ponding, roadwork occupation) and special vehicles (dangerous goods, uncovered gravel trucks, emergency vehicles), outputs natural-language event descriptions graded by severity, notifies traffic, police, fire and medical agencies simultaneously, and tracks each incident until traffic resumes.

VLM output

An aerial view of a two-lane forest highway shows a long queue of stopped trucks and vans backed up in one direction, with two dark cars pulled onto the right shoulder and a few people standing on the shoulder beside them. The camera descends closer over successive frames, revealing the stopped truck convoy, skid-like marks on the pavement, and the small group of people gathered next to the two shoulder-parked cars while the opposite lane still carries moving traffic.

  • Incident DetectedYes
  • Incident TypeBlocked Lane
  • Traffic Queue PresentYes
  • Stopped Vehicles Count8+
  • Pedestrians On RoadwayYes
  • Pedestrian Count3
  • Road Debris VisibleNo
  • Emergency Response PresentNo
  • Hazard SeverityModerate

Customer challenges

1

Footage is reviewed after a crash is reported, not understood as it happens

2

Legacy AI cannot judge crash type, severity, injuries or whether responders have arrived

3

Debris, stalled vehicles and uncovered loads in view are not recognised until a secondary crash

4

Dangerous-goods and uncovered gravel trucks are not identified or tracked across cameras

5

Agencies receive separate calls with no shared scene understanding

6

Incident timelines and evidence are compiled by hand from scattered footage

Customer benefits

Crashes detected the moment they happen and all agencies notified together; rescue starts earlier, within the OHCA golden window

Suburban crash proactive detection rate 80%; urban responder arrival time cut 50%

Debris and stalled vehicles known 8 minutes earlier; incident mitigation time cut 20%, fewer secondary crashes

Debris prevented at source: uncovered gravel trucks and dangerous-goods vehicles tracked proactively

Crash severity and rescue arrival reasoned automatically, grounding command decisions with full evidence

Existing CCTV and bus cameras reused; integration and go-live within 30 days

Key features

Accident Analysis

Analyzes accidents on the monitored road.

Road Hazard Detection

Detects hazards on the road surface.

Specialized Vehicle Detection

Detects specialized vehicles such as dangerous goods carriers.

Before & after

Before Suburban crashes surface 15–20 minutes after they happen After — with Linker AI Nexa Proactive detection rate 80%, alert in minutes
Before Urban responder arrival waits on citizen calls After — with Linker AI Nexa Responder arrival time -50%
Before Suburban reporting depends on passers-by After — with Linker AI Nexa Reporting efficiency +90%
Before Debris and stalled vehicles found after a secondary crash After — with Linker AI Nexa Awareness 8 minutes earlier, mitigation time -20%
Before Severity, injuries and responder status confirmed by phone After — with Linker AI Nexa 100% of crash events auto-graded into structured fields within a minute
Before Each incident record compiled by hand After — with Linker AI Nexa Time per incident record -70%

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