As a true and instant detection of our environment is crucial for our car, we use spatial sensors. The installation of these sensors on the car, the analysis of the sensor data and the integration of the results in our software framework are the responsibility of the 3D Lab. The team members of 3D-Lab are introduced here.

The main challenge is that our algorithms must perform in real-time, and as well must be 100% reliable. If the object detection fails for just a second, this may cause a crash of the car. So we maintain a 3D map of our environment, which is updated every 40 milliseconds and uses the input of several independent sensors. 


LIDAR (Light Detection And Ranging) is a method for measuring distance by light. A beam of infrared laser light (not harmful to humans) is emitted, and may be reflected by an object and return to the sender, while its time of flight is measured. So by knowing the speed of light, the object's distance to the reflector can be computed.

3d mapMost LIDARs work as scanners, where the laser beam is redirected by a rotating mirror receiving unit, or the electronics itself rotate. As they are active sensors and use a wavelength which is not contained in the sunlight, they work independendly of the lighting conditions, at night just as well as in bright daylight. LIDARs are excellent in determining the position and shape of an object, but as a relatively new technology it is  rarely used in the automotive industry so far. The following LIDARs are mounted on our prototype cars:

The Velodyne (TM) laser scanner, which we use for localization and obstacle detection. It delivers 1.6 million 3D points per second. The IBEO Lux (TM) laser scanner system consists of 6 individual sensors and a fusion box. It looks 200m around the vehicle. The IBEO Alasca (TM) laser scanner looks for obstacles and is looking ahead up to 200m. The SICK LMS (TM) laser scanner looks at the street curb and detects edges and lane markings.


RADAR (Radio Detection And Ranging) uses echoes of electromagnetic waves of the radio spectrum. Unlike LIDAR, accurate positioning is not possible, because radio waves don't spread straight. The object's speed can be determined using the Doppler effect. This method was used as early as in the World War II in order to control missiles. In the automotive industry, RADARs are commonly used for an automatic cruise control, for lane change assistants and as emergency brake system. In our car, the 'MadeInGermany', we have integrated the following kinds of RADARs:

The SMS (TM) radar is a short-range radar and operates at 24GHz. We merge the obstacles with the ones detected by the Lux LIDARs. The Hella (TM) radar system was originally  developed for the VW Phaeton and its special use is to observe the neighbour lanes. The TRW (TM) is our radar sensor with the longest range and it reliably detects other vehicles. It is crucial for fast driving on highways.

Obstacle DetectionObject recognition

The main task of the 3D sensors is the obstacle detection. Based on a high-precision map, we extract the geometrical road surfaces and match them with our sensor input. This "area of interest" is monitored in order to detect dynamic or static obstacles.

The sequence of data processing is straightforward: Initially, the raw data is filtered and clusters are aggregated. Then all clusters are examined and features (accessible areas, obstacles, road markings) are extracted. Obstacles are tracked and updated with new input data in any time frame, and their movement is predicted by a Kalman filter. Even so-called „ghosts“ are filtered here. From the list of tracked obstacles, the CN-Lab will derive the correct behavior.


Lane Detection and Localization

Laser lane detection Besides the spatial information, also the intensity of the reflected wave is provided by the laser scanners. The data is comparable to brightness values of a grayscale image, but in the infrared spectrum used by the sensors's emitter. In contrast to a camera, LIDAR images are independent from the lighting conditions as LIDARs are active sensors. On the other hand, cameras have a higher resolution and can be purchased at lower prices.

The intensity information is used to detect road markings. Based on this information we can determine our lateral offset and correct the position data provided by our GPS System. Further, an intensity map of the road surface and the surrounding is generated, and features as gullies, asphalt cracks and so on are extracted. When travelling the same road again, the map features are matched with the live data input, and this way the position of the car can be determined. Also intensity histograms, and 3D features (vertical edges) are tracked and stored in a map in order to enhance the localization reliability. The long term aim is to enable a positioning method independent from any GPS data.

BMBF FU Berlin Format Unternehmen Region