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

Abstract

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.