Title :
Recognition of dangerous situations within a cooperative group of vehicles
Author :
Batz, Thomas ; Watson, Kym ; Beyerer, Jiirgen
Author_Institution :
Inst. for Inf. & Data Process., Fraunhofer IITB, Karlsruhe, Germany
Abstract :
We consider the recognition of dangerous situations in vehicle traffic. Unscented Kalman filters are used to predict vehicle trajectories within a short prediction horizon [t0, t0+ Deltat]. Based on this prediction, for each vehicle pair the mutual distance is computed for [t0, t0+ Deltat], whereby the distance accounts for the geometric distance, for the prediction uncertainties as well as for the spatial dimensions of the vehicles. If at least one of the mutual distances falls below a distance threshold isin within [t0, t0 + Deltat], then a dangerous situation arises for the cooperative group and may lead to an autonomous cooperative driving manoeuvre. This approach allows the usage of the system in a mixed environment (only some vehicles are cooperative and cognitive). Obstacles can also be handled. The key issues in this ongoing research work are the recognition and classification of dangerous situations and the formation of a cooperative group constituting an operational unit. A common relevant picture within a group coordinator fuses the necessary information from all cooperative vehicles of the group and forms the basis for situation recognition and classification. This paper is a step to expand a Cooperative Collision Warning System (CCWS) to an integrated Cooperative Collision Avoidance and Cooperative Collision Mitigation System (CCAMS).
Keywords :
Kalman filters; collision avoidance; road safety; road traffic; autonomous cooperative driving manoeuvre; cooperative collision mitigation system; cooperative collision warning system; cooperative group; dangerous situations recognition; distance threshold; geometric distance; integrated cooperative collision avoidance; mutual distance; prediction uncertainty; short prediction horizon; situation recognition; spatial dimension; unscented Kalman filters; vehicle group; vehicle pair; vehicle traffic; vehicle trajectories; Alarm systems; Collision avoidance; Collision mitigation; Fuses; Mobile robots; Remotely operated vehicles; Road accidents; Sensor systems; Trajectory; Vehicle driving;
Conference_Titel :
Intelligent Vehicles Symposium, 2009 IEEE
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-3503-6
Electronic_ISBN :
1931-0587
DOI :
10.1109/IVS.2009.5164400