Europe: 12-minute lead time on detection of accidents and collisions

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case study
Challenge
A European motorway operator faced significant challenges from operator alert fatigue, due to a high volume of automated system alarms. Many were not critical or actionable, often going ignored by the control center. The challenge was to reduce noise while retaining detection coverage, speed and accuracy, to improve operator engagement, response times, and safety outcomes for the road.
Objectives
“With Lanternn, the operators trust the alerts. The system often provides the earliest warning of accidents on the road, even in areas without cameras. When an alert comes in, we always pay attention.”
— Traffic control center analyst
Impact
During the pilot, Lanternn drastically reduced the number of alarms operators needed to process while keeping a high detection rate. By combining multiple data sources and dynamically filtering out irrelevant reports, the system allowed operators to focus on actionable events, improving overall safety, efficiency, and speed.
Key outcomes:
During the pilot, Lanternn achieved:
- 85.5% true positive rates (based on operator assessment), filtering out 97% noise
- 42 additional valid events raised daily (equivalent to 3× the prior volume of valid events)
- 12-minute lead time on detection of accidents and collisions
- 100% operator engagement
Solution
Lanternn was deployed across a 24 km section of the motorway, integrating data from multiple sources:
- CCTV cameras (static and PTZ)
- Valerann CV AID (automatic incident detection) for stopped vehicles and hazard lights
- Crowd-sourced traffic data
- Weather data integration
The system processed over 2,000 daily raw detections, which were filtered to ~60 actionable events per day, achieving a 97% reduction in noise. By fusing data from multiple sources, Lanternn ensured that only the most relevant incidents were shown.
Outlook
Extrapolating these results to the full ~240 km network, Lanternn could:
- Raise ≈574 alerts per day, of which ≈412 new, actionable events per day would be identified
- Save over 11 hours per day in operator time by reducing manual monitoring and increasing automated prioritization
Conclusion
Lanternn not only addresses operator alert fatigue but also improves efficiency in incident detection and response. By filtering out noise and delivering only the most relevant alerts, Lanternn helps operators to act with greater speed and accuracy, reducing response times and improving road safety.



