Predictive maneuver evaluation for enhancement of Car-to-X mobility data

Advanced Driver Assistance Systems (ADAS) employ single object information to provide safety, comfort, or infotainment features. The required data is mainly extracted from external sensors to recognize and predict the future states of relevant traffic participants. Next generation ADAS will also use data from additional sources like, e.g., Car-to-X communication networks, to avoid some typical restrictions of common sensor setups. In this work, we present a method, which uses information on other traffic participants, and furthermore recognizes and considers their interactions in terms of traffic maneuvers to better predict their states. For this purpose, a probabilistic framework is presented, which recognizes object interactions as well as different road characteristics by introducing local, adaptive occupancy grids. The resulting maneuver recognition is shown to considerably improve received mobility data in terms of position, speed, and heading. These concepts have been fully implemented and evaluated by means of real world experiments.