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Continental adds predictive capabilities to ADAS with Road Database

Published: September 15, 2015 | Frankfurt, Germany

Continental ITS division delivers Road Database that can supply highly accurate route information to ADAS platforms. The basic idea behind Road Database is for information to be taken from vehicle hardware sensors, and used to generate a machine-readable representation of the road. Depending on the purpose for which it is required, the system is designed either to complement conventional digital street maps or to offer an alternative.

Today’s ADAS platforms  such as dynamic e-Horizon developed by Continental, require highly accurate information about the roads being driven on. In some cases, they include much more detailed attributes than just road geometry and lane guidance information – for example, variable speed limits, mobile construction sites, and slope data. This data helps to improve the driver assistance systems or optimize the engine output. However, it is for highly or fully automated driving that such road information becomes indispensable.

The existing method of producing highly accurate maps based solely on surveys and data entry by professional cartographers is laborious, expensive, and time-consuming.

Continental_ITS_Road_Database_ADAS

The Road Database solution developed by Continental exploits the fact that the sensors already found in modern vehicles (cameras, radar and, to an increasing degree, LiDAR) are continuously recording a vast amount of the information required. The road data is first processed to extract features of the road in the Road Database format. The in-vehicle Road Database software then compares this data against any information that may already be stored in the vehicle for the same piece of road. The newly identified differences are then anonymously and securely transmitted to the backend operated by Continental. The data volumes that are generated are comparatively small. The backend system of the Road Database can receive information from a large number of vehicles and, in a second processing stage, consolidate this data in the cloud and make it more accurate.

During this process, the server indexes all information, whether road geometry or “furniture” (road signs, traffic lights, etc.), according to its reliability. If, for example, several vehicles confirm modified lane guidance or a new road sign, recognition errors can be quickly ruled out. Data is shared using a secure method using pseudonyms. The highly accurate route information obtained can then be fed back wirelessly to the vehicles connected to the system. The data can then be used locally by the advanced driver assistance systems and as a basis for further refinement and updates using the on-board sensors. Classical navigation functions such as street names or points of interest (POI) are not the focus of this concept.

Source: Continental

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