Uncertainty Estimation of Location Information under Vehicle-Vehicle Cooperative Control

With the rapid development of vehicle intelligence and communication technology, vehicle-vehicle cooperative technology plays an increasingly important role in intelligent transportation systems. The core of vehicle-vehicle cooperative technology is the vehicle movement state data collection and interaction. In traditional vehicle data processing, GPS data is mostly filtered, and the output is a single set value. In this study, an extended Kalman filter was used to filter out the uncertainty of vehicle's motion state data, except for a single determinant, and the approximate distribution of the data was obtained. Through these distributions, the Monte Carlo method is used to calculate the safety evaluation indexes including time to collision, time headway, and safety margin. Fitting the evaluation index with the common distribution, the results show that the safety evaluation index is subject to the Burr distribution.