Cooperative Vehicle Position Estimation

Abstract

We present a novel cooperative vehicle position estimation algorithm, which can achieve higher levels of accuracy and reliability than existing GPS based positioning solutions by making use of inter-vehicle distance measurements taken by a radio ranging technology. Our algorithm uses signal strength based inter-vehicle distance measurements, road maps, vehicle kinematics, and Extended Kalman Filtering to estimate relative positions of vehicles in a cluster. We have preformed analysis of our algorithm examining its performance bounds, computational complexity and communication overhead requirements. Also, we have shown that the accuracy of our algorithm is superior to previous proposed localization algorithms.