A Cascaded Learning Framework for Road Profile Estimation Using Multiple Heterogeneous Vehicles

Abstract

Road profile information can be utilized to enhance vehicle control performance, passenger ride comfort, and route planning and optimization. Existing road-profile estimation algorithms are mainly based on one single vehicle, which are usually susceptible to modeling uncertainties and measurement noises. This technical brief proposes a new cascaded learning framework that utilizes multiple heterogeneous vehicles to achieve enhanced estimation. In this framework, each individual vehicle first performs a local estimation via a standard disturbance observer (DOB) while traversing a considered road segment. Then learning filters are designed to dynamically connect the vehicles, and the preliminary estimates from one vehicle are utilized to generate the learning signal for another. For each vehicle, a heterogeneous learning signal is produced and added to its estimation loop for estimating enhancement, through which the estimations are improved over multiple iterations. Extensive numerical studies are carried out to validate the effectiveness of the proposed method with promising results demonstrated.