Using AI to Solve the Problems of Shared Micromobility

Alex Nesic
6 min readJul 31, 2020


If one were looking for silver linings in these trying COVID times, micromobility has emerged as a preferred method of transportation in urban environments, naturally providing fresh air and physical distancing. However, that doesn’t mean there aren’t still kinks to be worked out. If you were to ask operators and cities the question ‘what are the biggest issues with dockless LEV programs’, you would get some fairly divergent answers touching on various themes like operational efficiency, unit economics, regulatory compliance, data sharing, and insurance requirements — just to name a few.

Probably the most critical issue both sides would agree on is that user behavior management is an ineffective tool in producing the desired outcome in any scenario, be it following the rules of the road, wearing helmets, or parking (and sometimes locking) scooters where they should be. It is an undeniable truth that some humans find particular pleasure in eschewing (sometimes purposely) rules with which they are unfamiliar or deem inconvenient — in favor of greater personal convenience or expedience. In the world of dockless micromobility, this produces less than optimal outcomes, sometimes involving injury or fatality, and often resulting in regulatory penalties and ADA violations. Ironically, the scrutiny under which operators and users of micromobility find themselves is draconian compared to the standards upheld for automotive user behavior under similar circumstances. I personally see this higher standard of safety and responsibility as an opportunity to solve some of these issues with technology.

Is it art…? Dott scooter precariously parked in Place des Vosges, Paris

The good news is that Artificial Intelligence (AI) is proving itself to be quite capable at mastering the type of boring yet impactful decision-making in the micromobility space that humans often seem unwilling to do — i.e. obey regulations. Adding to the human aspect of the problem is that these regulations often vary from one city to the next, making it even more difficult for users to comply consistently, even if they want to. Operators, data aggregators, and ancillary startups are already using AI to enhance their understanding of collected data with the goal of optimizing rebalancing, predicting demand, and performing preventive maintenance. While these worthy pursuits will increase efficiency and profitability, I want to elaborate on a different type of applied AI — one that delivers edge-based ability for LEVs to recognize their position precisely in an urban environment coupled with real-time vehicle control to optimize operational efficiency and prevent regulatory abuses. While this type of AI is ubiquitously used in the pursuit of Level 5 autonomous driving, it has not yet been applied to micromobility.

Until now, that is. Our team at Drover AI leverages and continues to advance cutting-edge AI techniques and proprietary sensor fusion algorithms in the patented technology that powers our flagship product, PathPilot.

Drover PathPilot

PathPilot is an easy-to-install ‘dash cam’ IoT module for micromobility vehicles that uses Drover’s proprietary tech to enable granular infrastructure distinction in real time. Out of the box, the PathPilot can accurately and reliably identify the following three infrastructure categories: sidewalk, street, and bike lane. This feature alone dramatically enhances geo-fencing capabilities in a granular manner that existing GPS-based solutions simply cannot, particularly in dense urban environments where it matters most. The most pertinent application of this precise awareness is real-time sidewalk detection and the subsequent vehicle control it enables.

PathPilot sidewalk control (inset is PathPilot POV)

Adequate biking infrastructure falls short in most cities, and it is clear that additional investment in dedicated, safe bike lanes would produce better outcomes — as many cities are doing during COVID. However, even adequate infrastructure won’t stop scofflaw behavior entirely — bikes and scooters will still be ridden on sidewalks, moped users will illegally use sidewalks, bike lanes and park paths. Some argue that the incidence of sidewalk (or other illegal) riding is overblown but the fact remains, solving for edge cases in transportation typically produces a more equitable overall solution and product — without detracting from the broader experience.

Beyond the regulatory and safety value of real-time vehicle awareness in finite environments, Drover’s PathPilot can verify proper parking in real-time where ‘corrals’ are used as designated parking areas. This technology reduces the need to rely on user generated photos to end a ride and increases operational efficiencies. PathPilot can also recognize fallen scooters and flag them for corrective action — providing street teams immediate information to retrieve and relocate assets.

Parking garage distinction

Drover’s PathPilot can be trained to recognize additional, specific types of infrastructure based on operator and city needs — which can be leveraged to mitigate vehicle loss and theft. Recently, a city DOT inquired about the possibility of restricting scooter use in covered parking structures. Their concern was that scooters were being used to perpetrate theft and vandalize property in parked cars. For operators, this feature also has considerable value. When vehicles are ridden into such structures (or buildings), their GPS signal becomes significantly compromised, and leads to many person-hours wasted in search of those vehicles — which are sometimes never located.

PathPilot’s precise vehicle awareness also provides valuable data insights to operators and cities. Currently, tracking of vehicle movements is only as good as the GPS information provided — PathPilot can provide distinction even beyond ‘lane-level’ to include sidewalk, bike lane, and street level route data. This data can be used by operators to better understand fleet behavior, by cities to help inform bike-friendly infrastructure planning, and by insurance companies to better assess risk and offer more dynamic pricing.

Finally, the last two years of shared scooter deployments have been marked by regulatory expectations being met with obfuscation and placation by companies with little appetite (or resources) for the development of an effective solution. Where operators can’t proactively meet requirements, Drover PathPilot offers a uniform solution for cities looking to manage regulatory compliance across multiple operators. PathPilot is MDS compliant, vehicle agnostic and designed to be retrofitted onto any variety of LEV. Deployed across multi-modal fleets — bike-shares, scooters, mopeds or other — PathPilot effectively enables cities to monitor and enforce all shared micromobility equally on their preferred mobility data aggregation platform.

The last article I wrote — in January of 2020 (which somehow feels like a decade ago) — postulates that e-scooters would better serve cities as a more integrated part of public transit. We now live in a drastically different world. COVID has accelerated the need for cities and operators to work together — to align their objectives and collaborate more closely. We see this collaboration with companies like Tortoise who specializes in remote teleoperation of scooters for rebalancing, and Zoba with their predictive technology for optimizing operations and demand — both are innovating to benefit shared micromobility.

Technology, and AI in particular, has a valuable role to play to bring together public and private interests in the evolution of micromobility as a long-term urban transit solution. Drover is not only ‘here for that’, but intends to lead the AI revolution in micromobility.



Alex Nesic

Micromobility veteran, enthusiast and evangelist. Co-Founder and Chief Business Officer of Drover AI. #bancars