The automotive navigation of tomorrow is highly connected and capable of dynamically using high-resolution map information and vehicle and environmental data from the cloud. It is an enabler for powerful driver assistance, intelligent e-mobility and autonomous driving. As a global provider of software engineering services for the mobility industry, Intellias is involved in many of these developments.

Although obtaining mapping and navigation data is easier today than it was 10 years ago — thanks to dashcams, UAVs and satellites — collecting this data is still labor-intensive. Even if most corners of the world are already recorded in public and private geographic information systems (GIS), the maps still need to be maintained regularly. Data accuracy and timeliness are the two biggest challenges in the mobility industry, followed by coverage, as the physical world is constantly evolving. To meet these requirements, the evolution of navigation and digital mapping is gathering pace. The following six technology and deployment trends will drive automotive mapping and navigation in the coming years.

1. Enriching Mapping Data With AI

Satellite imagery was a breakthrough for map creation. The wrinkle, however, is that most mapping software cannot work directly with satellite photos. Visual data first needs to be codified into comprehensive navigation datasets in a suitable format such as the Navigation Data Standard (NDS). Then map owners must keep it up to date. Both processes are costly and labor-intensive, making them great use cases for artificial intelligence (AI) in mapping.

AI algorithms improve the speed and precision of digital map building by offering the ability to update maps more regularly and map new areas faster. They can classify objects in satellite images — buildings, roads, vegetation — to create enriched 2D digital maps as well as multi-layer 3D map models. With precise maps, you can delight users with better ETAs, detailed fuel or energy usage estimates, and richer point-of-interest information. 

Apart from facilitating the collection of mapping data, AI can also help with generating such data. Researchers from MIT and the Qatar Computing Research Institute (QCRI) recently released RoadTagger. This neural network can automatically predict the road type (residential or highway) and number of lanes even with visual obstructions present, such as a tree or building. The model was tested on occluded roads from digital maps of 20 U.S. cities. It correctly predicted the number of lanes with 77% accuracy and predicted road types with 93% accuracy. 

That said, sensor data collection from connected vehicles isn’t going anywhere. OEMs are increasingly relying on their fleets to collect new insights for digital map creation, and this process is becoming easier with advances in machine learning. HERE Technologies recently presented UniMap — a new AI-driven technology for faster sensor data processing and map creation. The new solution can effectively extract map features in 2D and 3D formats, then combine them with earlier map versions. This unified map content data model allows new digital maps to be available in 24 hours.

2. NDS.Live: From offline databases to distributed map data systems

Conventional onboard navigation systems are designed, developed and integrated with proprietary databases, which become obsolete with every new product generation. NDS.Live is the new global standard for map data in the automotive ecosystem, promoting the transition from offline to hybrid/online navigation. It minimizes the complexities of supporting different data models, storage formats, interfaces and protocols with one flexible specification. NDS.Live is not a database, but a distributed map data system.

NDS.Live was co-developed by global OEMs and tech leaders, including Intellias, Daimler, HERE, Denso, Renault and TomTom are among those who have already adopted it. For example, second-generation Mercedes-Benz User Experience (MBUX) systems are powered by NDS.Live. The distributed map data system provides fresh information for the driver assistance system, which gets visualized as augmented reality (AR) instructions on the head-up display (HUD). NDS.Live can help massively improve the navigation experience for EVs and regular connected vehicles. It also helps OEMs deploy value-added subscriptions for assisted driving and navigation.

3. 3D and HD map generation

3D maps enable accurate rendering of physical objects in a three-dimensional form. High-definition (HD) maps feature detailed information about road features (lane placements, road boundaries) and terrain type (severity of curves, gradient of the road surface). Both types of maps are essential for launching advanced ADAS features and, ultimately, ushering in the era of autonomous driving.

3D maps define how the vehicle moves and help it interpret the data it receives from…