Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lowercost and morescalable solution spurred the idea of using sensors on board vehicles for a continuous and networkwide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with lowcost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve submeter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates.

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.