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Traffic

Road Safety Education

Road Safety AI focuses on dangerous behaviour, near misses and crash precursors. Most road AI answers only 'is there a car, a pedestrian, a crash?'; a VLM goes further and asks whether this person, this vehicle and this behaviour constitute a risk in the current road environment. By analysing CCTV over long periods the system accumulates the large volume of dangerous behaviour and near misses that never appear in crash records, maps them onto the Digital Twin as a road-safety Risk Map, and the AI Agent helps managers understand why a location is dangerous rather than only that a crash once happened there. Digital Twin scene clips feed directly into licensing, campus and driver-training road-safety education.

VLM output

An elevated highway camera looks down on a dry, multi-lane carriageway in daylight where several dark pieces of debris lie scattered across the rightmost lane near the edge line. Cars and a dump truck continue past at speed, some passing directly over or beside the debris without stopping, and one dark object is thrown up alongside a white car in the first frame.

  • Road Hazard PresentYes
  • Hazard TypeRoad Debris
  • Debris Pieces Visible5-8
  • Unsafe Behavior ObservedYes
  • Unsafe Behavior TypeDebris Straddling
  • Pedestrians On RoadwayNo
  • Collision OccurredNo
  • Vehicles In View8-12
  • Traffic FlowFree-Flowing
  • Lighting And WeatherDaylight, Dry

Customer challenges

1

Only crashes are recorded; near misses and dangerous behaviour in the footage are never captured

2

Legacy CV cannot read the interaction of people, vehicles and road context as risk

3

Failure-to-yield, box blocking and obscured signals are not detected systematically

4

Managers know where crashes cluster but have no visual evidence of why

5

Road improvements cannot be verified before and after with observed behaviour

6

Safety education lacks realistic scenes reconstructed from real intersections

Customer benefits

From post-crash outcome analysis to pre-crash risk analysis

A Near Miss Dataset that did not exist before

Identifies the truly high-risk locations and behaviour patterns, with video evidence for road improvements

Quantifies the change in dangerous events before and after improvements, with AI continuously validating results

Lower cost of watching large CCTV estates by hand

Performance defined by crash prevention (A1 and A30 reductions), establishing data-driven road safety management

Digital-twin education material lets road users see the risk before the crash

Key features

Failure-to-Yield to Pedestrians Detection

Detects vehicles failing to yield to pedestrians.

Intersection Accident Analysis (Type, Vehicles, Injuries, Responders)

Analyzes intersection accidents, including type, vehicles involved, injuries, and responders.

Near-Miss Detection (Vehicle–Pedestrian Conflict, Hard Braking)

Detects near-misses such as vehicle–pedestrian conflicts and hard braking.

Dangerous Driving Behavior Detection (Wrong-Way, Illegal Parking, Pedestrian Intrusion)

Detects dangerous driving behavior including wrong-way driving, illegal parking, and pedestrian intrusion.

High-Risk Intersection Condition Detection (Box Blocking, Roadwork Narrowing, Obscured Signals)

Detects high-risk intersection conditions such as box blocking, roadwork narrowing, and obscured signals.

Risk Hotspot Mapping & Safety Insight Generation (Digital Twin)

Maps risk hotspots and generates safety insights in the digital twin.

Road Safety Education Video Generation (Digital Twin)

Generates road safety education videos from digital twin scenarios.

VLM Safety Understanding

Uses a vision language model to interpret safety-relevant scenes.

Dangerous Behavior Detection

Detects dangerous behavior in the monitored area.

Incident Understanding

Interprets what happened in a recorded incident.

Risk Event Classification

Classifies risk events by type.

Safety Digital Twin

Provides a safety digital twin of the monitored site.

AI Safety Agent

Provides an AI safety agent to assist review and response.

Safety Insight Generation

Generates safety insights from accumulated events.

Before & after

Before 0% of near misses recorded After — with Linker AI Nexa Continuous near-miss capture on 100% of monitored intersections
Before Crash awareness via citizen calls After — with Linker AI Nexa 80% proactive detection, responder arrival -50% (crash scenario)
Before CCTV reviewed only after a crash After — with Linker AI Nexa 60–125x more feeds under continuous analysis
Before Failure-to-yield evidenced by occasional patrols After — with Linker AI Nexa 24/7 evidence on 100% of monitored crossings, with weather, severity and pedestrian type
Before Improvements judged by crash counts years later After — with Linker AI Nexa Before/after change in dangerous events measured within months
Before Safety education built on generic material After — with Linker AI Nexa Scenes reconstructed from real intersections, already used in licensing and campus courses