Bayesian Cooperative Relative Vehicle Positioning using Pseudorange Differences

F. de Ponte Müller, E. Munoz Diaz, B Kloiber and T. Strang

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 inter-vehicle communication link. In this paper we analyze two competing strategies against eachother: 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 is extended in a second ?lter using on-board inertial and speed sensors following a multisensory fusion approach. The performance is evaluated in both, a highway and an urban scenario. The multisensory fusion approach proves to be able to stabilize the baseline estimate in GNSS challenging environments, like urban canyons and tunnels.
Published in: Proceedings of IEEE/ION PLANS 2014
May 5 - 8, 2014
Hyatt Regency Hotel
Monterey, CA
Pages: 434 - 444
Cite this article: Müller, F. de Ponte, Diaz, E. Munoz, Kloiber, B, Strang, T., "Bayesian Cooperative Relative Vehicle Positioning using Pseudorange Differences," Proceedings of IEEE/ION PLANS 2014, Monterey, CA, May 2014, pp. 434-444.
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In