Background: A 15% increase in ride-hailing trips can put 59% more vehicles on the streets, 30% of which are empty (and the services have no incentive to maximize vehicle utilization). And a recent study by the San Francisco Transportation Authority found ride-hailing services are responsible for 51% of the city’s traffic slowdown over the past six years. New York City has already capped ride-hailing licenses in response to traffic concerns.

What's needed: To curb crowding, cities could implement centralized control of AVs — like air traffic control for planes — to direct vehicles from multiple manufacturers and service providers.

• While airplanes have different onboard hardware and software, all aircraft connect to a single control system, most of which is now automated.
• A similar system could capture demand and evenly distribute vehicles, pooling travelers and managing key performance indicators such as wait and travel times.
• To get all parties onboard, including carmakers and mobility providers, the control layer would need to be a vehicle-agnostic platform that could coordinate and optimize fleets in real time. The engine powering the platform would provide data on resource management and service operations that would benefit all players.

Where it stands: The industry is starting to pay attention.

• A Deloitte article earlier this year argued that “cities likely need a comprehensive, interoperable system that transcends existing infrastructure, drives standardization and interoperability.”
• As Here Mobility's Liad Itzhak put it: “Smart cities will need a platform, since mobility isn’t their core service. They’ll need the R&D that connects all the different mobility services independently.”

The bottom line: Shared AVs promise to make transit cheaper, safer and faster, with fewer vehicles moving more people. A centralized infrastructure would help realize this promise.

Raphael Gindrat is co-founder and CEO of Bestmile.

For autonomous vehicles, the greatest lesson from other shared mobility options is that playing nice matters. To deploy new services — and eventually AV technologies — companies will have to learn to work with cities.

• UberCab became Uber to obey the letter of a cease-and-desist notice, if not its spirit. Bird, Lime and other companies tried to take a similarly fast-and-loose approach by deploying scooters on streets without engaging city policymakers — but were quickly met with impounded scooters and lack of access to permits.
• Cities now see the necessity and benefits of active regulation. San Francisco’s permit application process incentivized equity, safety and accountability. LA’s proposed standards — which will allow shared access to data like trip length and utilization — highlight the more sophisticated tools cities will have at their disposal when crafting regulations.
• Cities have experimented with bike and scooter regulation, with approaches ranging from “free for all” to utilization-based caps.

Companies hoping to operate AV fleets will likely have to show how they will improve safety, minimize increases in congestion and include all members of the community (especially young, old, disabled and low-income residents).

• In the meantime, AV companies and cities will have to build tools and platforms to enable more advanced regulation. This creates opportunities for startups and automakers alike.

Why it matters: By creating regulatory frameworks that promote competition, limit fleet sizes and ensure equity and accessibility, cities will shape the future business potential of AVs While this may put a short-term damper on deployment and adoption, it should lead to AV fleets and services that are better integrated into their communities.

Anne Widera is a mobility consultant and former strategist at Uber's Advanced Technologies Group and Google's Self-Driving Car Project. She is also a member of GLG, a platform connecting businesses with industry experts.

Maps for humans, like those displayed on a smartphone, rely on GPS and simple receivers that are accurate to a few meters. But they can afford to be a little imprecise — even though the map doesn't know the difference between a sidewalk and a street, it trusts that you do.

To compensate for their inability to operate safely and reliably from context alone, AVs need more of the scenes they encounter to be pre-mapped. No amount of sensor data can substitute: Maps tell an AV there’s a traffic light coming, for instance, even when a large truck blocks the view. Up-to-date maps also let AVs reroute to avoid tough situations like unprotected left turns or intersections under construction.

These machine maps must meet several key demands:

• Incredible precision, so the car can compensate for its lack of understanding context and know where it is within 10 cm.
• Granular instructions, like which lane the car is in, the traffic rules that apply to that lane, and even overhead clearances and road elevation.
• Constant connection, which continues to provide information even when GPS signals are weak or missing.

The catch: Collecting accurate 3D data of cities and keeping the information on them up-to-date have both historically been incredibly expensive and time-consuming. Even a thorough one-time map is almost useless for autonomy because cities are constantly evolving.

What to watch: Since autonomy deploys in small pockets around the globe, shrinking the cost and size of sensors (some combination of cameras, lidar and motion-capture data) to create and update these maps is crucial to meeting market needs. The challenge is making the hardware footprint lighter and scalable, while also delivering sufficient accuracy.

Neehar Garg is head of product at Mapper.ai.

Go deeper: Why machines need maps of their own