Study on performance of networked dead reckoning for real vehicles

Vehicle-to-vehicle (V2V) communication technology based on Wi-Fi technology has recently been used to prevent vehicles from colliding with each other. However, in V2V communications, radio waves cannot be diffracted well enough at intersections at which vehicles cannot detect each other. Therefore, such technology might not prevent traffic accidents from occurring. In this study, we used mobile networks to exchange location information, such as longitude and latitude, every 100 msec and enable vehicles to use the information to predict the locations of surrounding vehicles of near future and avoid traffic accidents in any situation. However, the LTE network will become overloaded with radio resources, and data fees might become too expensive. Therefore, we have to reduce the amount of data packets to 2% and maintain location accuracy in the location-management servers at the same time. In this study, by using networked dead reckoning and our vehicle motion model, we developed a data reduction/compression and communication method to reduce the uploading frequency of location information from clients to servers in the LTE network and maintain the accuracy of location prediction at location-management severs.