Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Application of a driver intention recognition algorithm on a pedestrian intention recognition and collision avoidance system

: Diederichs, Frederik; Brouwer, Nina; Klöden, Horst; Zahn, Peter; Schmitz, Bernhard


Bengler, Klaus (Hrsg.) ; Bundesministerium für Wirtschaft und Energie -BMWi-:
UR:BAN - human factors in traffic : Approaches for safe, efficient and stress-free urban traffic
Wiesbaden: Springer Fachmedien, 2018 (ATZ/MTZ-Fachbuch)
ISBN: 978-3-658-15417-2 (Print)
ISBN: 978-3-658-15418-9 (Online)
ISBN: 3-658-15417-9
Bundesministerium für Wirtschaft und Energie BMWi
19S12009J; UR:BAN
Urbaner Raum: Benutzergerechte Assistenzsysteme und Netzmanagement
Aufsatz in Buch
Fraunhofer IAO ()

Driver intention recognition can enhance the driver-vehicle interaction by offering more intuitive assistance and automated driving support. Especially urban environments require fast reactions and hence assistance systems which act in accordance to driver’s intentions. Assistance should provide comfortably timed warnings only in situations when drivers really need this support and not in situations when the driver is already intending to react to a thread.
Fraunhofer IAO developed an algorithm in the UR:BAN MV subproject VIE to detect driver’s intention to brake when passing a pedestrian. The Fraunhofer algorithm analyses eye gaze data in correspondence with pedal activity to judge the driver’s attention on the pedestrian and the readiness to brake. BMW implemented the algorithm in the UR:BAN KA subproject SVT in a research vehicle and combined it with an environmental analysis of the situation.
In a test scenario the timing of a warning to the driver was adapted to the recognized intention to brake. Together with BMW’s pedestrian intention recognition algorithm, the driver intention recognition allows early warnings, while limiting the frequency of warnings to really relevant situations.