Luna provides AI and computer vision to manage how micromobility vehicles are ridden and parked

Luna’s solution provides real time and irrefutable confirmation to operators and cities that scooters are riding in the right lane, avoiding pedestrians and are parked correctly. Luna’s ‘camera as a sensor’ solution also generates valuable and actionable data insights for a variety of smart city applications.

Urban Mobility Safety Platform

Compliant parking

We offer an “AI Parking Selfie” SaaS solution that enables irrefutable and real-time 'virtual docks' for operators, using predefined visual clues.

Sidewalk riding prevention

Luna's computer vision AI enables road/sidewalk/cycle lane recognition.

Mobile mockup, Luna Systems

Pedestrian Detection

Luna enabled scooters can detect and count pedestrians and react accordingly in real time.

Camera as a Sensor

We enable operators and cities to turn shared micromobility fleets into mobile sensor networks, capable of detecting, monitoring and reporting key parameters - from lane blockages to heat mapping of sidewalk riding, and from critical infrastructure monitoring to road condition surveys.

SOLVING E-MOBILITY CHALLENGES

OUR TECHNOLOGY

SIDEWALK DETECTION

Is the scooter being ridden on a road, or on a footpath/sidewalk? Should the rider be warned?

PEDESTRIAN/OBJECT DETECTION

How many pedestrians are in the scooters path? Should the rider and/or pedestrians be alerted?

AI/Computer Vision

We trust our eyes to find a location more than we trust the GPS blue dot on our screen. Luna computer vision/AI technology is far more accurate than the standard GNSS technology being used in the micromobility industry. Operators and municipalities simply cannot be sure that its rental fleet can be governed correctly by GNSS.

Operators globally are scrambling to improve the accuracy of the positioning of their fleets, and are experimenting with higher accuracy GNSS, new positioning algorithms (dead reckoning), as well as ‘sensor fusion’ methods to collect multiple data points from other sensors on the scooter, in order to more accurately ‘guesstimate’ its position.

These improvements still fall down when compared to computer vision/AI, because they are ultimately based on legacy GPS, which is an inherently inaccurate technology for a variety of reasons. Most shared scooter operators are starting to realise this, and more importantly most large cities are starting to reject GPS based solutions as they see them as entirely inadequate.

Luna technology can give stakeholders real time, accurate confirmation and control of how scooters are being ridden

AI parking selfie

This is a new, very low cost, highly effective way to ensure riders park their free floating scooters/bikes correctly in virtual docks, at the end of each ride. The computer vision enabled parking verification algorithms, are creating irrefutable and real-time ‘virtual docks’ for operators, using predefined visual clues.

This is an app-based solution (no hardware requirement), but we will also be offering this parking verification/compliance capability as part of the Luna sidewalk riding detection hardware module.

The ‘AI parking selfie’ tech obviates the need for expensive fixed corrals, removes the perceived need for lock-to ordinances by cities, and also avoids docking station RFP stipulations from municipalities that lower utilization rates.

There is no need to LiDAR map thousands of parking locations in each city, and no need to rely on highly inaccurate geofences in order to manage your parking locations. You just need some painted markings on the ground – whether they be an operators bay markings or an existing city car park space with scooter signage.

AI parking detection

Luna addresses the challenges slowing the adoption of micromobility in urban environments

Luna is a unique mix of telematics, cloud, AI/computer vision software, hardware, electronics & smart city skills

About us

Founding team

Andrew Fleury

CEO

Andrew has spent his career to date in the field of telematics, fleet management and intelligent vehicles. Andrew has spent 15 years as the CEO of Transpoco, the leading telematics provider in the Irish market, with customers is more than 60 countries.

Andrew has a keen interest in the future of transport, mobility and unmanned autonomy.

He is currently the president of the 1-Telematics Alliance, a European thought leadership group. He is also on the steering committee of both the “Vehicle Of The Future” (VOTF), and the “Connected and Autonomous Vehicles” (CAV) industry groups in Ireland.

Ronan Furlong

CBO

Ronan is the Executive Director of DCU Alpha, Dublin City University’s Innovation Campus, which is Ireland’s leading cluster of IoT, M2M, Data Analytics and e-mobility companies.

Ronan is a qualified Architect and Smart Cities/Sustainability expert and sits on the advisory committee of Smart Dublin. He also advises governments, municipalities and real estate developers on innovation district/cluster and e-mobility policy development.

Ronan has a B.Arch from UCD, a MBA from Trinity College Dublin and a Dip. Sustainable Development from DCU.

Micromobility for leading brands

Our clients

Testimonials

What people say?

This is such an important research pilot project for TIER in Ireland and we are excited to have launched this trial across the five campuses of Dublin City University. It is an exciting opportunity for detailed research on smart city applications of e-scooters as well as modal shift, as we partner with Luna and Insight to help the University to reduce its carbon footprint and offer a more sustainable, safer first and last mile public transport solution. We hope to apply all project learnings to future TIER operations in Ireland.
Zipp is delighted to be partnering with Luna, another rapidly emerging Irish micromobility success story, to make our fleet the most technologically advanced one on the streets of the UK, and in the near future Ireland.

Contact us

Get in touch for any inquiries

Phone: +353(0)1 90 53 881
Email: info@luna.systems

DCU Alpha Innovation Campus, Glasnevin,
Dublin, D11 KXN4

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