OREANDA-NEWS. UR:BAN (German acronym for Urban Area: User-Focused Assistance Systems and Network Management) is the first research initiative to address complex urban traffic situations. At the mid-point of the initiative established in 2012, Daimler AG together with the 30 other partners involved in the initiative presented promising results in Brunswick.

The goal of the projected funded in part by the German Federal Ministry of Economics and Technology (Bundesministerium fur Wirtschaft und Energie) is to develop intelligent and cooperative driver assistance and traffic management systems to make urban traffic safer, more stress-free and more economical. Prof Dr-Ing Ralf Guido Herrtwich, Head of Corporate Research and Advanced Engineering Driver Assistance Systems of Daimler AG, emphasises: "The UR:BAN initiative perfectly matches our "Real-Life Safety' strategy, with which we want to prevent accidents and mitigate their consequences. The intelligent fusion of sensors and systems helps in detecting potential dangers in urban traffic and reacting to them in a timely manner".

The focus of Daimler's involvement in UR:BAN is the 'cognitive assistance' research pillar. The objective: new assistance systems are supposed to provide driver support in complex situations such as at junctions with pedestrians and cyclists, in tight spots, with oncoming traffic and when changing lanes, for example. Herrtwich adds: "With our involvement we want to further improve the safety of all road users. New assistance systems will significantly reduce the hazards for less protected road users such as pedestrians, cyclists or wheelchair users".

To date, the warning and emergency braking function of production-ready assistance systems is limited to frontal collisions with pedestrians and cyclists in straight-line driving. As part of the project to "protect weaker road users", existing methods for the image-based recognition of pedestrians were now enhanced to detect as many road users as possible - even when making a turn. This includes pedestrians in an unusual position or stance, partially obscured pedestrians, riders of two-wheeled motorised vehicles, wheelchair users or playing children.

The basis for this are methods developed during the course of the project, which combine empirically determined behaviour patterns with detail information obtained from sensors, such as the direction in which pedestrians look, the orientation of their heads or their leg movements. When merged with sensing of the surroundings and context knowledge, such as the motion of the own vehicle, the course of the lane or the unoccupied space, this allows determining the potential danger more precisely and robustly.