Have you ever missed the on-ramp for a freeway because you were in the wrong

lane? Or had to circle a block in search of a package drop box or specific

entrance? These moments may just be minor, albeit annoying, inconveniences for

us as individuals. But for autonomous vehicles, ride sharing, or logistics

companies that make thousands of daily trips and deliveries, the costs of such

navigation errors add up quickly.

Companies that depend on high-quality maps for precise navigation have two

off-the-shelf solutions:

Regular map applications like those used by individual
consumers do not provide enough detail and users can’t layer their own data
on top of these maps in a scalable way.
HD map companies offer more turnkey solutions but customers
still can’t edit or update the data themselves because the maps are owned by
the mapping companies. Furthermore, it’s a time-consuming process to get
maps updated and customers must wait for vendors to cover new regions.

Lacking a viable off-the-shelf option, many companies opt to build their own

maps. Scaling the process to generate large volumes of extremely precise

annotated map data poses the same challenges as scaling data pipelines for any

other AI application.

Introducing: Scale Mapping

Scale Mapping gives customers the most

flexible, scalable, and transparent mapping solution. With Scale Mapping,

customers can generate high-precision maps down to the centimeter level for

simulations and real-world testing, enhance prediction and motion planning by

effectively predicting another agent’s intent, train perception models to

live-detect map features, and improve navigation and route planning to

maximize efficiency.

How It Works:

High-precision annotations of large-scale maps are extremely challenging. To

minimize cognitive load for human annotators (Taskers) and to maximize

labeling efficiency and accuracy, Scale Mapping first breaks map sections down

into tiles. From there Taskers start with vector annotations, or annotations

of physical map features such as lane boundaries and crosswalks. Once the

vector annotations are complete, semantic annotations — or the annotation of

concepts — such as the location of package drop boxes or a bike lane becoming

a turn lane is layered on. Lastly, relationships between map features and map

semantics are linked, such as a traffic light to a specific traffic lane.

After all three layers of annotations are complete, the individual map tiles

are stitched back together to create a comprehensive map.

Real-World Impact

Because it can support data inputs from LiDAR sensors, cameras, and even

existing maps, this workflow allows customers to develop, update, and own

their own proprietary HD maps at speed. Scale Mapping is already trusted by

leaders in the autonomous driving industry.

Nuro, one of the leaders in the self-driving delivery space, and Domino’s,

the largest pizza company in the world based on global retail sales,

launched autonomous pizza delivery in Houston. Nuro’s R2 robot is the first

completely autonomous, occupantless on-road delivery vehicle with regulatory

approval from the U.S. Department of Transportation.

Scale partnered with Nuro to improve perception and mapping models for the

R2, starting with providing high-quality, rapidly labeled data. With Scale

Mapping, Scale is moving up the autonomous driving stack by enhancing

localization and mapping capabilities. Put simply, the R2 needs to know

where it is, what’s around it, where it’s going, and how to get there. The

data Scale provides lets the R2 do exactly that while ensuring the safe,

accurate, and efficient delivery of pizzas.

In terms of the impact across our Nuro projects, we doubled the amount of

data annotated from 2020 to 2021.

Talk to our team today for a demo and more information about our

industry-leading quality with transparency, flexibility, and rapid

turnaround.