Where and why errant behaviour happening in your city? You would need visual data to really understand this. We apply computer vision to understand and appropriately eliminate the root causes of three core dangers of shared mobility scheme:
Accurate location tech is imperative - GPS inaccurate for LEVs (Urban Canyon Effect) – operators do not know if a scooter is being ridden on a road versus a sidewalk. Irrefutable visual proof of exact location = Control
Luna enables cities and operators to work together withcontext.
Context allows for appropriate actions!
Is someone mounting sidewalk on purpose? = example actions: automatically reduce speed & penalise
Is it due to fear (narrow infrastructure) - educate, reroute, incentivise
Context allows us to Learn and fix core behaviour or infrastructure deficits causing errant vehicle use.
Beyond safety - visual insight on how car-less infrastructure should evolve based on rider use.
Provides irrefutable visual proof of real-time location of e-scooters/e-bikes, allowing operators to eliminate sidewalk riding, collisions and disorderly parking.