Traffic Monitoring

Digital Traffic Twins by 61 GHz Radar

Monitoring of Railway Crossings

Many accidents at railway crossings (guarded or unguarded) could be avoided - the new KY-RAY 3D.02.04 traffic Radar detects road users (truck, cars, motorcycles, cyclists and pedestrians) at low investment cost and high integrity due to redundant measurement.
Upon train approach activation, the Radar will provide a warning message when obstacles are still the monitoring zone, to allow early train operator warning at a safe distance.

The KY-RAY 3D.02.04 Radar sensor does not have any moving parts and is completely maintenance-free.

Redundant monitoring of level crossings
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Traffic Radar at intersection
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Avoid turning accidents
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Traffic Control at Intersections

KY-RAY 3D.02.04 Radar sensors, in combination with cameras, can optimze the traffic analysis and control.
The Radar detects all road users (truck, cars, motorcycles, cyclists and pedestrians) in complete darkness, with wet, reflecting street surfaces, direct sunlight or snowy conditions - situations, where cameras typically have their technical limitations.

Main applications:

Detection, qualification and counting of road users
Early information about dangerous situations (road blocks, accidents, vulnerable road users in close proximity)
Acquisition of traffic density (dynamic green light phase skipping)

Technically, Radar does not recognize any number plates or faces - it is absolutely safe regarding the protection of personal data.

Safety Monitoring of Road Shoulders

The traffic Radar KY-LOC 3D.02.04 can continuously monitor road shoulders. Road users parked or stranded on the shoulder are detected even in complete darkness. Safety alerts can be transmitted to traffic control with details and exact locations to guide any services.

Monitoring of road shoulders
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Support environmental perception of autonomous vehicles
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Autonomous Driving

The future use of autonomous vehicles on highways or inner city roads will be supported by the KY-LOC 3D.02.04 traffic Radar, in order to detect environmental situations beyond reach of the sensors onboard the autonomous vehicles. This enhancement of the environmental perception will be transmitted over low latency 5G networks and allows autonomous traffic participants to make better driving decisions.