Cross-traffic, cyclists, crossing pedestrians, perhaps engrossed with their s..."/>Daimler UR:BAN pedestrian safety research project comes to a close

Automotive

Published on October 7th, 2015 | by Daniel Sherman Fernandez

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Daimler UR:BAN pedestrian safety research project comes to a close

Cross-traffic, cyclists, crossing pedestrians, perhaps engrossed with their smartphones, mothers with buggies, children playing – city traffic places demands on drivers in many different situations while at the same time posing risks of accident. Plenty of scope for assistance systems that support the driver in addition to making urban driving safer and less stressful. On the way to that goal, 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.
Assistenzsysteme für die Stadt: Gefahren sehen und erkennen wie der Mensch - Eine Verkehrssituation mit Fußgängern, Radfahrern und alle Objekte sind eindeutig detektiert und erkannt. Dank Szenen-Labeling. Driver assistance systems for the city: seeing and recognising dangers in the same way as a human - A traffic situation with pedestrians and cyclists and every object is clearly detected and identified. Thanks to scene-labeling.
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. 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.
Assistenzsysteme für die Stadt: Gefahren sehen und erkennen wie der Mensch - Eine Verkehrssituation mit Fußgängern, Radfahrern und alle Objekte sind eindeutig detektiert und erkannt. Dank Szenen-Labeling. Driver assistance systems for the city: seeing and recognising dangers in the same way as a human - A traffic situation with pedestrians and cyclists and every object is clearly detected and identified. Thanks to scene-labeling.
Consequently, the system functions in a manner comparable to human sight. This, too, is based on a highly complex neural system that links the information from the individual sensory cells on the retina until a human is able to identify and differentiate an almost unlimited number of objects. Scene labelling transforms the camera from a mere measuring system into an interpretive system, as multifunctional as the interplay between eye and brain.
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This system is really drawing on the technology that Volvo developed with their City Safety package, that was subsequently made available for all manufacturers to adopt and use in their own luxury cars. The systems have been emulated in Japanese cars as well, although we will always remember that Volvo were the first to get the ball rolling- and despite the changes their company has undergone, Volvo will always be the last word in safety.
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