Bayesian Cooperative Relative Vehicle Positioning using Pseudorange Differences

Abstract

Forward collision warning systems, lane change assistants or cooperative adaptive cruise control are examples of safety relevant applications that rely on accurate relative positioning between vehicles. Current solutions estimate the position of surrounding vehicles by measuring the distance with a RADAR sensor or a camera system. The perception range of these sensors can be extended by the exchange of GNSS information between the vehicles using an in inter-vehicle communication link. In this paper we analyze two competing strategies against each other: the subtraction of the absolute positions estimated in each vehicle and the differentiation of GNSS pseudoranges. The aim of the later approach is to cancel out correlated errors in both receivers and, thus, achieve a better relative position estimate. The theoretical analysis is backed with Monte-Carlo simulations and empirical measurements in real world scenarios. Further on, two Bayesian approaches that make use of pseudorange differences are proposed. In a Kalman Filter pseudorange and Doppler measurements are used to estimate the baseline between two vehicles. This concept is extended in a second filter using on-board inertial and speed sensors in a multisensor fusion approach. The performance is evaluated in both, a highway and an urban scenario. The multisensor fusion approach proves to be able to stabilize the baseline estimate in GNSS challenging environments, like urban canyons and tunnels.