Qualcomm and TomTom to crowdsource HD mapping data for autonomous driving


Qualcomm Technologies, a subsidiary of Qualcomm Incorporated, has announced that it is working with TomTom, the Netherlands-based provider of traffic information, navigation, and mapping products, on using the Qualcomm Drive Data Platform for high-definition (HD) map crowdsourcing, in order to accelerate the future of autonomous driving.

Qualcomm’s Drive Data Platform uses cutting-edge technologies to intelligently collect and analyze data from different vehicle sensors, supporting smarter vehicles to determine their location, monitor and learn driving patterns, perceive their surroundings, and share this perception with the rest of the world reliably and accurately.

TomTom’s HD Map, including RoadDNA, is a revolutionary, highly accurate, digital map-based product, which assists automated vehicles to precisely locate themselves on the road and help determine which way to maneuver, even when traveling at high speeds. The traditional development of maps requires deploying dedicated fleets of vehicles equipped with professional-grade sensors to collect location, raw imagery, lidar and other data, which is then transferred, stored and processed in data centers. Now that cars are increasingly connected and equipped with a range of sensors, new and complimentary approaches become possible.

Using the precise positioning, on-device machine learning, heterogeneous compute and connectivity capabilities of the Qualcomm Drive Data Platform, which features the company’s Snapdragon 820Am automotive processor, TomTom and Qualcomm aim to add an improved, scalable and cost-efficient crowdsourcing approach to the mix of sources for HD map making. The new concept is designed to allow massive numbers of connected cars to see and understand their environment, traffic and road conditions, and support real-time input for map and road condition updates.

Machine learning algorithms, running on the Snapdragon processor, are optimized using Qualcomm’s Neural Processing Engine toolkit, and are intended to identify features such as traffic signs and lane markers, and fuse this information with the precise positioning to generate feature-rich, highly accurate and lightweight map updates. This allows cars on the road to become live sensors that automatically update navigation services about real-time road conditions using the processor’s integrated X12 LTE modem.

“Feature-rich, highly accurate and frequently updated HD maps are critical to support some of the most advanced applications envisioned in the automotive industry, especially for autonomous driving, explained Willem Strijbosch, head of autonomous driving at TomTom. “We are building the cloud-based platform to make and maintain HD maps using a range of input sources, including crowdsourced data from swarms of intelligent connected vehicles. We’re excited to explore the connectivity and compute capabilities of the Qualcomm Drive Data Platform to help map the world for the future of driving.”

Nakul Duggal, vice president of product management at Qualcomm Technologies, commented, “We are demonstrating that an affordable and easy-to-integrate mapping solution for autonomous vehicles is realizable. Our Drive Data Platform is designed to integrate key technologies into a cost-effective edge compute solution required to support safer, highly connected and smarter transportation, and we are pleased to offer this technology for HD Map providers such as TomTom, as well as auto makers, shared mobility service providers, and the automotive industry at large.”

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About Author


Tom has edited Traffic Technology International (TTi) magazine and its Traffic Technology Today website since May 2014. During his time at the title, he has interviewed some of the top transportation chiefs at public agencies around the world as well as CEOs of leading multinationals and ground-breaking start-ups. Tom's earlier career saw him working on some the UK's leading consumer magazine titles. He has a law degree from the London School of Economics (LSE).