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.
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.
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.