AI in Micro-mobility

It's suddenly everywhere! Micro-mobility has re-defined neighbourhood-level transit with dedicated micro-mobility lane appearing in cities globally!

Micro-mobility has been a landmark shift in the history of transport which has historically witnessed a slow evolution from animal-driven to petrol-fueled to electric-powered and now, AI-enabled mobility.

As progressive urban planning approaches the idealized '15-minute communities' fulfilling residents' most daily needs within 15-minutes walking or cycling, mobility strategies are prioritizing short-distance oriented micro-mobility over traditional space- and capital-intensive vehicular infrastructure within communities. Micro-mobility include walking and single-person mobility such as bicycles / e-bikes, scooters / e-scooters, and LEVs (Light Electric Vehicle) with travel speed of under 25km/hr and under 500kgs.

In the current micro-mobility revolution, AI-enabled e-scooters and LEV (Light Electric Vehicle) are set to play a key role. While AI and ML-applications enable edge-based capability for autonomous vehicles (AV) in terms of positioning, real-time control, maneuvering, operational efficiency, maintenance, and asset security, these AI capabilities are now being integrated in to micro-mobility.

Integration of AI-enabled cameras and sensors allow e-scooters to sense the rider's surroundings, such as pavements, scooter-lanes, kerbs, pedestrians and other moving objects, and make real-time maneuvering decisions for riders. In combination with geo-positioning technology, the micro-mobility assets are able to assist in locating, maneuvering, verifying parking spots availability, and for operators to understand fleet use behavior for improving services.