Our technology

Accurate GPS material

Parking accuracy and dockless vehicle clutter caused by inaccurate GPS.

Traditional GPS is accurate to 5-10 meters, but Luna is accurate to about 10 centimeters. Luna uses an ultra-low-power, real-time kinematic (RTK) edge module to provide centimetre-level accuracy across GNSS (GPS/Glonass/Beidou/Galileo) satellite constellations. This gives positioning to about 10cm in reasonable conditions.

Rider behaviour and vehicle interaction with public realm.

Riding on sidewalks and/or operating LEV’s (such as delivery bots, autonomous lawn mowers etc, without due consideration for other road/sidewalk users, generates many problems. Luna uses high end automotive grade machine vision and ADAS, to help ensure safe riding and operations at all times.

Safety first

Full Stack Turn key solution

Luna offers a one-box solution to operators to purchase e-bikes or scooters with our IoT fully customizable to our customer’s needs. 

We can also accommodate operators in integrating Luna’s IoT with their existing vehicles.

Future growth

Full development

GNSS

The use of multiple GNSS constellations (as opposed to just GPS), results in there being a larger number of satellites in the field of view of the vehicle/device, which has the following benefits: Reduced signal acquisition time, Minimised influence of obstructions caused by buildings and foliage, Improved spatial distribution of satellites used, leading to less dilution of precision, and Improved positioning accuracy – more satellites equals more accuracy.

RTK

Real-time kinematic (RTK) positioning is a correctional technique used to enhance the precision of position data derived from GNSS constellations. It uses measurements of the phase of the signal’s carrier wave in addition to the information content of the signal, and relies on a single reference station or interpolated virtual station to provide real-time corrections.

Additional integration

AI & machine vision

To overcome ‘GPS black spots’ (e.g. urban canyons), Luna also uses machine vision and AI to recognize scooter parking spaces using simple 2D images (QR codes) located in and around the scooter parking site.

Road vs Sidewalk vs Bike lane recognition

Pedestrian & Obstruction identification

Helmet detection

Gait analysis of riders as a predictor of accidents

Smart City Insights (Surface conditions, near misses)

Pedestrian detection
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