NAVER LABS’ ACROSS is a project that has been initiated to develop a crowdsourcing map solution to maintain the recency of HD road maps.
"An HD map is the most essential piece of data required to enable autonomous driving on the road"
Precise HD maps are essential for an autonomous-driving machine. The HD maps allow you to better recognize your current location. The sensors equipped in the machine may sometimes not be enough to do that job. This prior knowledge can be useful when planning a route to drive and predicting which areas will have to be given more attention. Therefore, the importance of an HD map grows greater in a complex large city.
That is why NAVER LABS has continued to develop HD Maps with a unique technology called hybrid HD mapping to this day. Hybrid HD mapping is a method where a wide range of road layout information is first obtained through aerial photographs, then it collects and organically combines point cloud data on the road with R1, an independently developed mobile mapping system (MMS). This solution possesses the strength of allowing something on the scale of a large city to be constructed in a more cost-effective manner within a shorter period of time, while, of course, maintaining a high level of precision at the same time.
However, there is still something missing. It’s the destiny of all maps. Keeping them up to date. Maps basically reflect reality, but not the present. The time when a map was made will always be in the past. After this time, a new road may have appeared or a new building may have been built. Therefore, an updating solution is directly related to maintaining the precision of a map. (The same is true for the self-updating mapping introduced earlier, which is technology for keeping indoor maps up to date that utilizes robots and AI).
"The dilemma of crowdsourcing, a tradeoff between the costs and performance of sensors in the mapping device"
That is why hybrid HD mapping technology also requires an updating solution. The ACROSS project is research aimed at developing such a solution. We have selected the crowd sourcing mapping method. Through this method, mapping devices are installed inside multiple vehicles to simultaneously identify changes in road information over a wide scale. We are currently developing a solution that detects and updates changes in the road layout (land information, location of stop lines, road markers, etc.) or 3D information (traffic signs, buildings, traffic lights, streetlights, etc.) through the processing of image data collected by sensors inside mapping devices.
However, there remains a dilemma for us to overcome. It is that we have to make mapping devices highly compact with low-cost sensors (cameras, imu, gps). By doing this, they will be able to be equipped in more vehicles and the issues concerning the coverage of detecting changes in an HD map and its cycle can be addressed. However, designing a mapping device with low-cost sensors and processors will inevitably result in performance tradeoffs. In the end, device design to facilitate wide use and algorithm optimization constitute the core of the ACROSS project. To this end, a wide range of technologies developed by NAVER LABS, including sensor fusion, computer vision, image processing, and machine learning are being continuously applied.
5G networks also offer a new opportunity for ACROSS. By using the high bandwidth of 5G, a change in the environment where the map information can be received faster and updated simultaneously has been initiated. Above all, more attempts and choices have become available between the cloud and edge computing in order to achieve optimization between devices and algorithms.
"A world where high-precision 3D data on cities and roads are updated in real time."
We expect that there will be many trials and errors along the way towards the success of the ACROSS project. We remain relentless in our efforts to overcome challenges that have not yet been mentioned. However, it is important to remember that these are crucial trials and errors. Throughout such fierce challenges, the core technologies for HD maps and autonomous driving on the road will be ultimately acquired. For this year, we will focus on designing the most ideal mapping device for ACROSS and optimizing algorithms based on such findings. Once this step ends in success, we will attempt to undergo a more diverse set of semantic mapping steps.
Autonomous driving machines will form part of our lives in the future. HD maps for these machines will be there first, and then autonomous-driving machines will come with the ability to automatically update HD maps on their own. High-precision 3D data on cities and roads will create an organic, virtuous cycle. Even more precise and even more up-to-date. The ACROSS project is preparing such a world.
We will continue to share the progress and achievements of the ACROSS project.