Hybrid HD Mapping, A Map for Self-Driving Vehicles
Self-driving vehicles have many sensors. They drive autonomously by processing a vast amount of data collected through those sensors. There is, however, a part of self-driving vehicles that acts as both data and a sensor at the same time: that part is the HD map.
There is a reason we can describe an HD map as another sensor on a self-driving vehicle. Self-driving vehicles utilize an HD map, along with other sensor data, to improve the accuracy of its location and for planning routes more effectively and safely. In this sense, an HD map is an essential element for the performance and safety of self-driving vehicles. This is why we are focusing on developing a new solution for precision machine readable HD maps that can be used in self-driving vehicles.
The Hybrid HD Mapping technology we have unveiled is truly a unique solution. It is based on the organic integration of large-scale aerial photographs of each city together with data from a mobile mapping system. First, we extract information related to the layout of the road’s surface from aerial images. Then, we organically integrate a point cloud collected by R1, our proprietary lightweight mobile mapping system (MMS), which moves around that space. Compared to conventional HD maps constructed by MMS vehicles, our mapping process can reduce the production costs and lead time significantly. This can all be done while maintaining the degree of accuracy, of course.
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NAVER LABS is independently studying and developing self-driving vehicles and has attained a temporary permission for it from the Ministry of Land, Infrastructure, and Transport. In this regard, we can develop the Hybrid HD Mapping directly by testing and comparing our research results. We are also actively conducting research on our localization technology that utilizes HD maps. This technology allows self-driving vehicles to identify their current location accurately and safely, even in the densest parts of cities, where GPS signals are easily lost.
As more diverse self-driving machines and services are introduced, the importance of HD maps will only increase. More advanced and diversified HD-based algorithms can also be expected to appear. Through the Hybrid HD Mapping technology, we hope to introduce a new HD map solution which satisfies the needs of both maintaining data accuracy and keeping production costs reasonable.