With rideOS products, you can launch a ridehail or delivery autonomous vehicle service in a few days.
Many AV companies plan to commercialize their technology via robotaxi (or robo-delivery) services. However, the companies looking to deploy via this model have not focused enough on the “taxi” aspects of robotaxi.
We understand why this is the case: AV companies are laser-focused on trying to solve one of the most difficult and impactful engineering challenges of our generation. But in order to capitalize on the advantages the technology will bring, AV companies need to also begin learning the key lessons associated with commercial deployment. We are here to help -- at rideOS, we can help any autonomous company launch a robotaxi or robodelivery service or pilot in a matter of days.
Why AV Companies Should Be Piloting
Running a robotaxi service is much more complex than just having vehicles drive themselves and building an app.
To begin, AV companies will need to learn how to adapt their self-driving systems to serve the ridehail use case. For example, the last 100 feet of pickups and dropoffs can be a unique challenge for autonomy systems once you have removed the safety driver and need the vehicle to be able to safely pull over in dense environments and complete or initiate trips.
AV companies also need to learn how to operate a consumer-facing service. This includes operational components such as customer support (as well as driver- or vehicle-owner support) and mobile apps, as well as marketplace dynamics such as understanding the right balance of supply and demand, and how to price trips to achieve that balance.
And to bring the marketplace to life, AV companies need software that routes their vehicles and efficiently assigns them to ride requests across their fleets. This is occasionally overlooked given the initial limited scale of robotaxi deployments, but getting this right is essential, since keeping vehicles highly utilized, maximizing the proportion of “paid miles” they drive, is the central driver of the economic viability of a robotaxi service. Piloting via car sharing is insufficient -- it is essential to learn about the unique challenge of dynamic optimization when vehicles can reposition themselves, pool trips, queue trips, and drive to their requestors, across an entire fleet. These issues get more complex, and important, with scale, so it is essential to begin learning them as soon as possible, before scale is reached.
Too Much Build To Build, Not Enough Time
AV companies have a dilemma: all the lessons above are important to learn ASAP in order to build a viable robotaxi business, but there is simply too much to build in-house.
This is where rideOS can help -- our routing, optimization, and dispatch solutions can power your robotaxi service, allowing you to focus on building the AV software.
At rideOS, we’ve carefully crafted a team comprising the most experienced leaders from across the ridehailing and self-driving industries, who have spent decades building this exact technology. Among our ranks, we count: the former Head of Maps at Uber; the former head of Autonomous Vehicle routing at Uber ATG; the former Head of Strategy for Uber ATG; the former Head of Autopilot Maps at Tesla; and others who have led the deployment of software at scale at Google, Apple, Waymo, Cruise, Mapbox, and Daimler.
In simulations, our products routinely outperform our competition, regularly demonstrating efficiency improvements of 50%+ (i.e., completing the same amount of trips with 50% fewer vehicles without degrading rider wait times). We would be happy to discuss how our AV routing, optimization, and dispatch software can serve your use case (e.g., AV routing for self-driving vehicles).
How to Begin Piloting -- One Approach
Companies deploying a robotaxi service do not need to build all of the necessary components to support operation at scale immediately, but we don’t recommend waiting for Level 4 autonomy to be ready to begin pilot launches, experimentation, and iteration. Even if the company plans to deploy onto 3rd party networks, and not operate a proprietary network, these are lessons they will want to learn through in-house pilots or early integrations with their partner networks.
Here is one of our recommended paths towards iterative learning for a robotaxi service. It starts with doing test trips with your “autonomous” (still with a safety driver) vehicle, followed by gradually layering in the routing, optimization, and dispatch technology, plus the ridehailing operations.
There are many potential variations to this plan, but the key recommendation is to not wait for the technology to be ready before preparing for commercialization. When self-driving vehicles are ready at scale, they will present a massive potential economic advantage relative to human-driven networks. However, Uber and Lyft operate incredibly well-oiled marketplaces that drive high utilization for vehicles while keeping costs low for riders, and have spent a decade perfecting the user experience. It would behoove any AV company hoping to catch up with Uber and Lyft to begin learning some of these lessons, in parallel, in order to magnify the economic advantage they will have when their technology is ready -- or alternatively, to make sure they are not hamstrung by poor fleet optimization, user experience, or overall preparation.
Here at rideOS, we can help you get a pilot spun up quickly with our Universal MaaS Platform and our team of veterans from the autonomous and ridehail industry.