MultiVehicle Cooperative Local Mapping Using Split Covariance Intersection Filter Local mapping plays an important role in outdoor intelligent vehicle applications and multivehicle cooperative local mapping which takes advantage of vehicular communication can bring considerable benefits to this important task. In this paper, a multivehicle cooperative local mapping architecture using split covariance intersection filter (Split CIF) is proposed. In the proposed method, a vehicle can flexibly perform cooperative local mapping with other vehicles in decentralized way, without complicated monitoring and controlling of data flow among vehicles; fused maps can be shared freely among vehicles. An efficient and accurate implementation of the Split CIF is also introduced. A simulationbased comparative study demonstrates the potential and advantage of the proposed multivehicle cooperative local mapping architecture using Split CIF.

Accuracy Evaluation of Camerabased Vehicle Localization Cooperative maneuvers are of high interest within many V2X applications. The implementation of cooperative maneuvers require the accurate localization of the vehicles. Accurate localizations of the egovehicle will be provided by the next generation of connected cars using 5G. Until all cars participate in the network, unconnected cars have to be considered as well. These cars are localized via static cameras positioned next to the road. The scope of this paper is the implementation and evaluation of a system which provides the detection, tracking, and localization of vehicles for a cooperative maneuvers scenario. The application is the lane merge of vehicles where the vehicle localizations are used for the planning of trajectories. The observed vehicles are equipped with GNSS RTK units for their selflocalization which is the basis for the accuracy evaluation of the localization provided by the camera system.