These six tech trends are driving automotive mapping and navigation forward

Dashcams, drones, and satellites make it easier today than ten years ago to obtain mapping and navigation data. However, their collection is still labor-intensive. Even though most corners of the world are now already captured in public and company-owned geographic information systems (GIS), maps need regular maintenance and updating. Data accuracy and currency are the two biggest challenges in the mobility industry, followed by coverage, as the physical world is constantly changing. To meet these demands, the further development of navigation and digital mapping is accelerating. These six technology and application trends are driving automotive mapping and navigation in the coming years.

#1 AI for Enhancing Mapping Data

Most mapping programs cannot work directly with satellite photos. Visual data must first be codified into comprehensive navigation datasets in a suitable format like the Navigation Data Standard (NDS) and then kept up to date. Both processes are costly and labor-intensive and are therefore excellent candidates for AI use. AI algorithms improve the speed and accuracy of digital map creation, allowing maps to be updated more frequently and new areas to be captured more quickly. They can classify objects like buildings, roads, or vegetation in satellite images to create enriched digital 2D maps and multi-layered 3D map models. Such precise maps allow, for example, better predictions of arrival times or detailed estimates of fuel or energy consumption. Additionally, the collection of sensor data from connected vehicles will continue. OEMs increasingly rely on their fleets to gain new insights for creating digital maps, and this process becomes simpler with advancements in machine learning.

#2 Distributed Map Data Systems Instead of Offline Databases

NDS.Live is the new global standard for map data in the automotive ecosystem, promoting the transition from offline to hybrid or online navigation. It minimizes the complexity of supporting different data models, storage formats, interfaces, and protocols through a single flexible specification. NDS.Live is not a database, but a distributed map data system. Developed by global OEMs and technology leaders, including Intellias, companies like Daimler, HERE, Denso, Renault, and TomTom have already implemented the system. NDS.Live can significantly enhance the navigation experience for electric and traditional connected vehicles. Additionally, it helps OEMs offer value-added subscriptions for assisted driving and navigation.

#3 Creation of HD and 3D Maps

Three-dimensional (3D) maps allow the accurate representation of physical objects in three-dimensional form. High-definition (HD) maps contain detailed information on road features like lane arrangements or road boundaries and terrain characteristics like the tightness of curves or slope of the road surface. Both types of maps are essential for advanced ADAS functions and autonomous driving. 3D maps determine how the vehicle moves and help it interpret the information received from the built-in sensors. Since most sensors have a limited range, HD maps assist by providing the navigation system with additional information about road features, terrain, and other traffic-relevant objects.

#4 Simulations for Autonomous Vehicles

Autonomous vehicles require extensive road and route tests to pass safety checks. Manufacturers also need to simulate near-crashes without putting anyone at risk. Hyper-realistic virtual worlds can be testbeds for autonomous vehicles. The new generation of realistic 3D environments can be created with data from various sensor types to effectively convey all the details of the physical world to the algorithm. Existing visual 3D databases already contain realistic details for traffic signs, road markings, and road textures. With machine learning and deep learning algorithms, complex ADAS/AD scenarios can simulate realistic conditions.