Voi vehicles in the Norwegian capital will be installed with Drover’s PathPilot AI technology to tackle pavement riding and help the operator and local authority control how and where scooters are ridden and parked.
Micromobility operator Voi is working with Drover AI to deploy computer vision technology on its e-scooters in Oslo.
Drover AI’s technology uses machine learning and computer vision to identify whether the e-scooter is on the pavement, road or cycle lane, to help prevent pavement riding.
Computer vision trial
Voi trialled the use of computer vision on e-scooters in Northampton last year. This latest roll-out will see thousands of e-scooters in the Norwegian capital installed with Drover’s PathPilot AI technology.
PathPilot will boost Voi’s geo-fencing capabilities in the city producing results at a level that it claims existing GPS-based solutions cannot offer, particularly in a dense built-up environment like Oslo.
The technology, similar to the sensors used in autonomous vehicles, can also be linked directly to a scooter’s motor to automatically slow the speed of the vehicle when it enters forbidden rider zones, such as pavements.
“By incorporating AI into our micromobility offering we believe we can nudge riders towards better parking and riding practices”
PathPilot also has the capability to train its parking algorithm to spot if a scooter is parked correctly. Using the camera as a sensor, the technology can help Voi and Oslo City Council govern and control how and where scooters are parked.
“Voi’s vision for 2030 is for micromobility to become a staple of urban living across the globe,” said Fredrik Hjelm, co-founder and CEO of Voi Technology. “But we know that vision can only become a reality if the micromobility industry prioritises the safety of users, pedestrians and other road users equally. That’s why we’re working with Drover to tackle the issue of pavement riding once and for all.
“By incorporating AI into our micromobility, offering we believe we can nudge riders towards better parking and riding practices.”
Through a successful roll-out in the US, Drover AI claims that it has proven that, out of the box, PathPilot is adaptable and easily scalable to new environments without the need for excessive training or expensive labour-intensive pre-mapping.
Additionally, the fully European health and safety approved product, does not require availability of any GPS data to function, allowing for location awareness and the ability to take corresponding actions.
Demand for shared micromobility is high in Oslo, with 70 per cent of the city’s population downloading Voi’s app during the summer 2021. Most Oslo riders experience a safe, comfortable and efficient ride, but Voi said it is committed to using innovation to ensure that every e-scooter ride in the city is a safe one.
By collaborating with Drover, Voi will be able to build a record of where and how the scooters are being ridden in Oslo, helping to inform algorithms that can prevent pavement riding and enable better scooter parking.
“It’s clear that micromobility has a key role to play in a sustainable future for urban transport and we know AI can help solve some of the industry’s toughest problems”
PathPilot will also automatically deliver actionable insights on fleet use and rider behaviours which Voi can then share with Oslo City Council to help improve the service. This could see the location of e-scooters optimised to minimise the risk of pavement riding while PathPilot can also recognise fallen scooters and flag them for corrective action.
“It’s clear that micromobility has a key role to play in a sustainable future for urban transport and we know AI can help solve some of the industry’s toughest problems,” said Alex Nesic, co-founder and chief business officer at Drover AI. “We look forward to seeing what today’s news in Oslo will mean for the future of micromobility.”
Voi recently launched Voiager 5 and among the new additions to improve the safety and usability is a new dashboard design, an integrated phone holder and a more ergonomic handlebar design aimed at those with smaller hands. Analysis of repair cycles and quality assurance checks also informed the improved design.