We all know that sensors and high-precision maps are two indispensable basic elements. As the level of autopilot increases, more data suppliers for high-precision maps have more requirements. So what is the way to collect high-precision maps?

At Gaode’s data center in Changping, these questions were answered.

Two different collection vehicles

A Rong, General Manager of Auto Demand Business and Autopilot Technical Expert, told me that Gaode plans to complete the production of high-precision high-speed automatic driving level (HAD) high-precision maps of more than 280,000 kilometers at the end of this year, as well as national and provincial national/provincial roads. The ADAS level of high-precision map data collection; ADAS level data at the end of 2017 extended to more than 30 urban arterial roads, HAD level to the internal extension of the national highways and major cities.

PS: ADAS level high precision map accuracy is about 50cm level; HAD and above high precision map accuracy is about 10cm level.

In order to obtain these data, Gao De currently has two kinds of collection vehicles, which are used to collect high-accuracy map data for the accuracy of ADAS and HAD.

A collection truck for collecting ADAS level high-precision map data, with 6 CCD cameras installed on the roof. Five of these cameras are surrounded by a circle with a single camera at the top, each with 5 million pixels and a total of 30 million pixels. The position of the co-pilot in the car has a display screen for collecting data, and the chassis is in the position of the trunk for storing and processing data.

The top of the data acquisition vehicle for collecting HAD level high-precision map data is to meet the required 10cm level accuracy by assembling two laser radars (located at the rear) and four cameras (two front and two rear). The two schemes can be used to build three-dimensional models of road information such as signs, obstacles, and lane lines. Gao De's staff told me that the cost of the vehicle was more than 8 million RMB. The RIEGL 3D laser scanning system on board is also the highest level product currently available.

Of course, the data collected by these two kinds of vehicles is only used as a basis. Road information is constantly updated, and as the degree of autopilot increases, there is a higher demand for real-time performance.

Use the power of crowdsourcing <br> <br> highly accurate map data acquisition if only car to complete the acquisition, the effort required is clearly too large. After all, acquisition is only the first step, and the maintenance of subsequent updates is equally important.

Gao De's solution is based on self-containment and crowdsourcing. Needless to say, the number of high-dead collector vehicles will continue to increase, and the coverage will gradually cover more specific and complex roads.

In addition, Alibaba's semi-socialized commercial vehicles, such as the rookie logistics vehicle and the Shenzhou Special Vehicle, will also become sources of high-precision map data. Gao De's high-precision map data may also be preferentially applied to commercial vehicles with fixed routes such as logistics and sanitation.

UGC will also be a high-precision map data source. In Golder's plan, there will be many rear-mounted hardware with rear-view mirrors, driving recorders, and other cameras, which will be able to return photos with road data to Gao De as a supplement to high-precision map data.

In addition, the increasing number of sensors in vehicles, including laser radars, millimeter-wave radars, cameras, gyroscopes, rain sensors, etc., can return information necessary for high-precision maps, including road conditions and weather conditions.

Of course, this requires that Gold German itself has the ability to process these data. At present, they are also conducting deep learning based on image data analysis (such as identification of road signs, speed limits, etc.), and a great deal of data has been obtained. As the sample size increases, the recognition accuracy will continue to increase.

By itself +

By combining third-party data, Goold wanted to improve the accuracy and real-time performance of high-precision maps as much as possible. At present, this is indeed a good idea. However, if you really want to achieve it, you need more investment, including technology, capital, and so on.

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