IEEE Access · 2019 · 21 citations · 18 references
ComputingAutomotive EngineeringAutonomous SystemsAutonomous VehiclesGlobal Navigation Satellite SystemsTransportationIntelligent Transportation SystemsSensor Fusion For LocalizationVehicle PerceptionVehicle-to-everything CommunicationGlobal Positioning SystemLocation SensingLocation-based ServicesPositionPositioning In Vehicular NetworksVehicle NavigationVehicle Position EstimationVehicle-to-vehicle Communication
One of the key components of the intelligent transport system is to provide a safe driving environment. The ability to quickly and accurately recognize the surrounding environments and neighboring vehicles is essential for the development of safety-related services to provide a safe driving environment. Currently, research in this field is being conducted based on the global positioning system (GPS), light detection and ranging, camera, and ranging sensors. However, the currently used sensors cannot recognize a wide range of vehicles because of their limited range of recognition. Moreover, GPS-based studies are highly affected by the surrounding environment because of the nature of GPS and have relatively high error rates and low rate of location information updates. The use of GPS-based location information for safety-related services can result in negative consequences. In this paper, we propose a new positioning system, called cooperative neighboring vehicle positioning system (CNVPS). The CNVPS rapidly identifies the locations of neighboring vehicles based on their information obtained through various sensors and shares this information with a wide range of neighboring vehicles over vehicle-to-vehicle communications. The CNVPS also compensates the position of a neighboring vehicle by applying the maximum likelihood estimation to the duplicated position observation of the other neighboring vehicles. The simulation results show that the CNVPS achieves approximately 370% improvement in location errors over GPS, assuming that the root-mean-squared error of GPS is 15 m. In addition, the proposed system has a location refresh cycle ten times faster than the existing GPS-based system.
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Tutorial on maximum likelihood estimation
In Jae Myung · Journal of Mathematical Psychology · 2003 · 1.8K citations
AN OVERVIEW OF THE OMNeT++ SIMULATION ENVIRONMENT
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Localization for mobile sensor networks
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Computing, Geoinformatics, Global Navigation Satellite Systems +14