triadacoffee.blogg.se - Singularity hidden map

Singularity hidden map update#

AV MaaS: L4-grade HD maps will still be a key factor for market entry in complex, urban domains in the years to come.

While many find their approach to “Full–Self-Driving” problematic (including the term itself), this leveraging of enhanced map information is useful to understand what is (and isn’t) possible with ~MD today. While they have spoken publicly about their aversion toward “HD maps,” Teslas today do use higher definition data than found in a conventional SD map (e.g., more information on things like lane counts, turning options, traffic control). One of the obvious responses to this might be: Isn’t Tesla already doing this without maps at all? The answer is - not quite.

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.

If these trends we’re seeing continue, there are a number of implications for stakeholders to consider to ensure they remain future-proofed: So we generated a hypothetical representation of that same intersection in a future MD state to illustrate: 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.

Singularity hidden map update#

We can then upgrade the MD data as necessary to update HD vectors in a base map that either we or a partner own. 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. This MD layer gave us great efficiency in establishing the scaffolding to detect and localize features, and changes to those features over time. 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. 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. Glimpses of our MD thinking can be seen from work we did with Toyota’s TRI-AD unit to assess how closely vehicle cameras alone can yield HD levels of fidelity. 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. The definition of “MD” is still very much in flux and inherently nebulous given its position in the fidelity continuum.

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. Take an example intersection, first mapped in SD:Īs 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. 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. To start, in the 2010s, the “high definition” (HD) map concept emerged as a mechanism for conveying key priors to aid machine-autonomy decisions.

We’ll use some of the slides from our presentations to unpack a bit of if here… 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. 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.

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. Over five years into CARMERA’s 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.