All Roads Are Not Created Equal…and Here’s How We Deal With It
By: Ro Gupta
Tags: Growth Strategy, Mapping, Autonomous Vehicles, Autonomous Cars, Perspective

Streamlining Our New-City Roll-Out Processes with Archetypal Thinking

Roads are not static structures. From major capital improvements to road repair to signage updates — roads are constantly changing.

Take San Francisco, for example. As part of its Vision Zero commitment, the city has recently embarked on a host of quick road improvement projects that will change things like stop lines, pedestrian crossings and lane markings across the city. These fast-tracked and “Quick Build” improvements come in addition to the city’s major, multi-year infrastructure projects — like the creation of designated bus corridors on the city’s main arterials.

Maps need to reflect these changes. For HD maps, up-to-date inventory data is critical to providing AVs accurate navigational and localization support. And for SD, consumer maps, too, this information is of value, allowing map providers to offer customers a richer navigational experience with things like natural language directions — think, “at the next stop sign . . . .” instead of “in 200 feet . . . .”

But cataloguing these changes presents an enormous operational challenge, especially as you try to scale coverage nationally — let alone globally. The move from expensive, cumbersome LiDAR-based change detection and/or aggregate consumer trace data to lightweight, scalable camera-based solutions is the first — and arguably most important — step. Camera-based change detection is between 30x and 100x more efficient, requiring less expense and fewer passes than LiDAR- and trace-data solutions, respectively.

But even if you can make this technological shift, being able to operationally ensure that you are “seeing” the street with the frequency necessary to reliably — and cost effectively — capture road changes is no easy task.

Building a detection portfolio

At CARMERA, we’ve adopted a portfolio approach, which gives us two sets of variables that we can flex in order to achieve maximum efficiency: First is the input modality — in other words, how we gather the data. We use two primary modalities — prosourced data capture via our commercial fleet partners and direct data capture via contracted, tasked drivers. Each offers different benefits in terms of cost and coverage.

Prosourced data collection is best suited for high-mileage coverage areas. By piggy-backing on the everyday drives of our commercial fleet partners, prosourcing allows us to get a lot of miles for a relatively low dollar-per-mile cost — there being no drivers to hire or vehicles to rent. However, in low mileage situations, prosourcing begins to provide suboptimal value. In these situations, dispatching contracted drivers on targeted runs proves more cost effective than supporting a multi-vehicle commercial fleet and processing excess and/or redundant video footage.

The second variable is the number of coverage vehicles — whether commercial fleet or contracted. If modality is a function of total mileage, then vehicle count is a function of average drive speed within an area of interest (AOI). The total miles any one driver can cover is a product of drive time — which tends to be a relatively fixed number — and average drive speed. In other words, all things being equal, a highly congested AOI will require more drivers than a traffic-free one, since each driver will be able to drive fewer miles in a given day thanks to the traffic.

Archetypes: matching the means to the map

Having these two powerful tools in our toolkit — input modality and vehicle count — the challenge becomes proactively knowing what combination to deploy under which scenarios. Let’s start with the metropolitan statistical area (MSA). Most of the country’s population lives in one of 384 U.S. government–designated MSAs — geographical areas with a dense urban core and common economic ties — and as such, they’re often used as a basic unit of analysis in the mapping world.

However, each MSA is unique. We’ve already discussed how road complexity — and therefore, an AV’s safety case — differs depending on the operational design domain (ODD). New York’s busy Fifth Avenue poses far greater challenges for an autonomous vehicle than Arizona’s Beeline Highway, for example. Here, however, we’re talking less about road complexity and more about the overall map characteristics.

In order to solve for this, we’ve categorized these 384 MSAs into 9 archetypes based on mileage and average drive speed, the latter being one of the data points we collect as part of our pre-mapping process. For those areas that have not been pre-mapped, we use population density as a proxy for average drive speed. Denser areas tend to have more traffic, and therefore, slower drive speeds than areas with less dense population. Each archetype is matched with a coverage solution, consisting of modality mix (i.e., percentage of prosourced vs. dispatched coverage) and vehicle count. While these can be tweaked based on an MSA’s unique characteristics (think, the unique traffic patterns created by the Las Vegas Strip), these coverage solutions give us a starting place, allowing us to streamline operations and rapidly deploy to new MSAs.

Take the San Francisco Bay Area, for example. With around 20,000 miles of roads, it’s in our middle tier in terms of total mileage — not quite New York–Newark (around 70,000 miles) but leagues larger than Flint, Michigan (around 4,000 miles). A blend of commercial fleets and tasked drivers is, therefore, the best fit.

For those of you familiar with the area, this makes intuitive sense. Commercial fleets regularly blanket the urban core — San Francisco proper, downtown Oakland, downtown Berkeley — as well as major arterials throughout the periphery; but smaller streets in the South Bay, say, or Marin County may not see such frequent travel. There, tasked drivers are more appropriate.

In terms of drive speed, the Bay Area is in the first tier with some of the highest congestion in the country. It’s, therefore, necessarily going to require more vehicles than, say, a Pittsburgh or a Poughkeepsie. Still we’re talking manageable numbers—somewhere around 10 vehicles total is sufficient.

Contrast this to the Chattanooga MSA, which straddles Tennessee and Georgia. With just under 10,000 road miles, it’s squarely in our lowest mileage tier. We know that its population density is low, so we can infer a high average drive speed; ergo, a low number of scanning vehicles will be required.

Ultimately, the name of the game is speed and efficiency. And by using these archetypes in conjunction with our portfolio strategy, we’re able to spin up coverage quickly and cost effectively. The success of the next generation of mapping and automotive technology is not just about big ideas and engineering brainpower; it’s also about the operational know-how to turn ideas into action, to bring theory into practice. And that’s one of our guiding principles as we map the road ahead.