Note: Woven Planet became Woven by Toyota on April 1, 2023. Note: This blog was originally published on April 9, 2021 by CARMERA, which is now part of Woven Planet. This post was updated on December 13, 2022.
By Ro Gupta, VP of Automated Mapping
A high falutin’ title to convey how maps are converging and fidelities blending toward a “medium definition” (MD) steady state, to empower humans with machine data, and machines with human intuition.
Over five years into our mapping journey, it has been both fascinating and illuminating to straddle two decades, each a unique era with distinct paradigms — moving from binary to convergent approaches toward map building for mobility.
Within autonomous driving, we started to see hints of this shift about a year ago, with a new “Hierarchy of Needs” taking shape, which we discussed last fall.
Since then, we’ve seen from the industry additional recognition of a related trend, which we’d been sensing for a while: Consumer, automotive and autonomous mapping needs are converging toward a common set of representational frameworks, prompting map integrators and data suppliers to rethink their strategies, while paving a path to accelerated deployment for the rest of the 2020s. Given the widespread corroboration we saw of these hypotheses from leaders across the mobility and mapping spectrum, we decided they’d be worth publishing for an even broader set of eyes. We’ll use some of the slides from our presentations to unpack a bit of if here…
The Recent Past & Present
To start, in the 2010s, the “high definition” (HD) map concept emerged as a mechanism for conveying key priors to aid machine-autonomy decisions. This machine-focused HD concept was presented in distinct — almost binary — contrast from “standard definition” (SD) digital maps created for human use, which were approaching ubiquity. Take an example intersection, first mapped in SD:
Now let’s take a look at the same intersection that we mapped in HD for Level 4 autonomous driving:
As you can see from the visuals and descriptions, there is a nearly 100x step-up in breadth, depth and accuracy to go from traditional SD to HD levels of fidelity.
The [Not-Too-Distant] Future
This bright-line distinction between SD and HD, however, is beginning to blur — the result of machine autonomy becoming more sophisticated and human applications becoming more demanding. A new “MD” standard is emerging. The definition of “MD” is still very much in flux and inherently nebulous given its position in the fidelity continuum. But in general, the power of MD comes from fusing the most critical elements of HD precision and insight, with the unmatched scalability of SD.
Glimpses of our MD thinking can be seen from work we did to assess how closely vehicle cameras alone can yield HD levels of fidelity. One of the requirements was to do so with no initial map at all, having to create an SD map out of vehicle trace data alone. The effectiveness and inherent scalability of such an approach underscores the building of our new Change-as-a-Service offering on top of what we call an MD (“medium definition”) map. This MD layer gave us great efficiency in establishing the scaffolding to detect and localize features, and changes to those features over time. It allows us to leverage existing SD maps, whether proprietary or open-source (e.g., OSM), or as noted above in the Toyota example, create them ourselves from vehicle telemetry if we need to. We can then upgrade the MD data as necessary to update HD vectors in a base map that either we or a partner own.
In the process of doing this, we suspected that with just a little more semantic information in the MD data, the map may be sufficient to meet the continually advancing perception and control capabilities of L2 autonomous driving stacks in the near term, and L4 in the longer term. So we generated a hypothetical representation of that same intersection in a future MD state to illustrate:
Note that while the feature data is pared down, it is still information-rich. For each traffic signal, for example, the map describes the order of control, the lane applicability of control and so on.
And indeed, it mirrored the sentiments we heard from companies across the L2 — L4 spectrum:
We don’t need every part of the map to be at HD levels of granularity or spatial accuracy like we used to require…
But we do need better than SD map information to inform key rules of the road decisions…
And we need it for millions of miles of road around the world, not thousands of miles within an urban geofence, or hundreds of thousands of miles for just a highway network…
We also need key changes to be reflected much more frequently than the months or even years it used to take to update our maps.
The simplified (work-in-progress) synthesis of all of this is as follows:
What Does This Mean?
If these trends we’re seeing continue, there are a number of implications for stakeholders to consider to ensure they remain future-proofed:
Auto OEMs: Traditional HD map building no longer should be an impediment to greatly expanding L2+ hands-off operating domains for increasingly discerning car buyers.
AV MaaS: L4-grade HD maps will still be a key factor for market entry in complex, urban domains in the years to come. However, for long-term expansion, robotaxi and delivery operators should ensure they’re paying close attention to what priors are necessary over time, and maintain stack versatility. As peers figure out how to leverage MD maps, it will present a major accelerant for them, and a significant gating risk to laggards.
Consumer Maps: One byproduct of the HD → MD evolution is that SD maps for human use also win, since MD can more feasibly mirror SD scale. We are already seeing enhanced functionality like natural language directions that allow drivers to “act more like a machine” when choosing where to, say, start a turn or pull over. Expect this to continue as SD maps evolve toward MD-grade in more and more places around the world, particularly given the lasting accelerants of COVID for prosumer navigation use cases like logistics and delivery.
Map Suppliers: Finally, as a company supplying map data, we have to adapt to the ubiquity and freshness challenges (100x improvements required on both). To do so, we have positioned ourselves to start from the SD <> HD poles, and coalesce toward an MD steady state over time to meet the industry where it needs to be in the future.
Join us!
We’re looking for talented engineers to help us on this journey. If you’d like to join us, we’re hiring!
Special thanks to co-authors/editors, Justin Day and Ethan Sorrelgreen, and to expert reviewers, Oliver Cameron, Muthu Kumar and Marc Prioleau.