Platoon Joint EKF for Improved Road Friction Estimation in Autonomous Platoons

Abstract

The advantages inherent to autonomous vehicle platoons render them a pivotal component in the evolution of intelligent transportation systems. Given the minimal spacing between vehicles in such a platoon, ensuring collision-free travel necessitates a more rapid estimation of the road friction coefficient. Capitalizing on the vehicle-to-vehicle (V2V) communication prevalent amongst platoon members, this paper introduces a platoon joint extended Kalman filter (EKF) estimator to expedite the trailing vehicles’ estimation of the road friction coefficient. A decision logic module is also designed within the EKF estimator of the following vehicles to process signals transmitted from the leading vehicle, achieving the objectives of signal assessment and reception. In the simulation scenarios presented in this study, the results of the platoon joint EKF estimation for the road friction coefficient, when compared to the results of an individual vehicle EKF estimation, show an improvement in estimation speed by 50% for butt roads and between 25%-80% for split roads. This attests to the efficacy of the proposed method, offering a reference for autonomous vehicle platoons to swiftly estimate road conditions and adjust inter-vehicle distances, thereby enhancing the safety of the platoon when road information changes.

Keywords

• Autonomous vehicle platoon
• extended Kalman filter
• road adhesion coefficient
• state estimation