ADASAutonomousConnected DriverConnected VehicleSmart Mobility

Daimler helps driver understand traffic situation with “scene labelling” ADAS feature

Published: October 08, 2015 | Stuttgart, Germany

Daimler researchers have achieved a breakthrough in connection with the UR:BAN research initiative. Using so-called “scene labelling”, the camera-based system automatically classifies completely unknown situations and thus detects all important objects for driver assistance – from cyclists to pedestrians to wheelchair users. Researchers in the “Environment Sensing” department showed their system thousands of photos from various German cities. In the photos, they had manually precisely labelled 25 different object classes, such as vehicles, cyclists, pedestrians, streets, pavements, buildings, posts and trees. Scene labelling transforms the camera from a mere measuring system into an interpretive system, as multifunctional as the interplay between eye and brain.


On the basis of these examples, the system learned automatically to correctly classify completely unknown scenes and thus to detect all important objects for driver assistance, even if the objects were highly hidden or far away. This is made possible by powerful computers that are artificially neurally networked in a manner similar to the human brain, so-called Deep Neural Networks.

At the closing event of the collaborative research project UR:BAN, short for “Urban Space: User-friendly Assistance Systems and Network Management”, the Daimler researchers presented convincing results from a total of five different test vehicles. In addition to a real-time demonstration of scene labelling, another test vehicle showed imaging radar systems and the new, fascinating possibilities they offer in urban environments. It was shown that radar sensors are now capable of comprehensively resolving and visualising not just any dynamic object, but also every static environment.

The third test vehicle included a system for the detection, classification and intention identification of pedestrians and cyclists. Similarly to a human driver, this system analyses head posture, body position and kerbside position to predict whether a pedestrian intends to stay on the pavement or cross the road. In dangerous situations, this allows an accident-preventing system response to be triggered up to one second earlier than with currently available systems.

A further highlight that was demonstrated was how radar- and camera-based systems can make lane-changing in city traffic safer and more comfortable. Following a command from the driver, this system provides assisted lane-changing in a speed range between 30 and 60 km/h. The system senses the environment as well as the traffic in the lanes. The situational analysis predicts how the scenario will develop and then enables the computed trajectory. This is followed by assisted longitudinal and transverse control for changing lane. The driver can tell intuitively from the instrument cluster whether or not the requested lane change can be executed by the system. After the change of lane has been successfully completed, longitudinal control with lane-keeping function is resumed. The driver at all times has the option of overruling the system by intervening with the steering, accelerator or brakes

The fifth test vehicle showed the potential for predicting driver behaviour in relation to planned lane changes or changes of direction. With regard to an imminent change of lane, for example, glances over the shoulder are linked with driving parameters that have already been sensed. A likely change of direction can be predicted from the interplay between steering movement, reduction of speed and map information. In the demonstration, the direction indicator was then automatically activated to inform other road users as early as possible.

Source: Daimler



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