MARV-X: Applying Maneuver Assessment for Reliable Verification of Car-to-X Mobility Data

Advanced driver-assistance systems (ADASs) employ single-object information to provide safety, comfort, or infotainment features. While today´s systems use common sensors, such as radars or cameras, to recognize and predict the future states of relevant traffic participants, next-generation ADASs will also use data from additional sources such as Car-to-X (C2X) communication networks. We present a method that uses information on other traffic participants and furthermore recognizes and considers their interactions in terms of traffic maneuvers. For this purpose, a probabilistic approach is presented, which identifies object interactions and different road characteristics. This method may find a particular application in the C2X domain for evaluating the mobility of neighboring vehicles based on received messages. In this paper, we present M aneuver Assessment For R eliable Verification of C2X mobility data (MARV-X), which is a tool embodying a two-stage process for reliable C2X mobility data verification. The first stage consists of a dedicated mobility estimator realized by a Kalman filter (KF). In the second stage, a plausibility check for highly dynamic traffic situations is applied using the advocated probabilistic traffic maneuver recognition. MARV-X is fully integrated into the vehicle´s C2X architecture. Its effectiveness is demonstrated by means of extensive real-world experiments.