Cloud-Based Vehicle Ride-Height Control

Abstract

We present a novel approach for a cloud-based ride-height control for vehicles equipped with an air suspension. The objective of this approach is to improve both efficiency and comfort, especially on single obstacles, by including vehicle-to-vehicle or vehicle-to-infrastructure (V2X) information on the road ahead in the control algorithm. The focus of this paper is the methodology of data processing on a cloud backend and includes three steps: pre-processing, clustering and allocation of streets to the clusters. In the first step, the database is reduced to obstacles relevant for driving comfort. The second step is to find clusters with a high density of obstacles on a road condition map. Finally, the probability of hitting an obstacle is calculated for each road in the area of a cluster, taking the characteristics and the topology of the road network into account. Example data is used to proof the functionality of the method. The proposed method seems to be a suitable approach for big data applications and might improve a vehicle ride-height control with regard to comfort and efficiency.