CloudBased Vehicle RideHeight Control We present a novel approach for a cloudbased rideheight 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 vehicletovehicle or vehicletoinfrastructure (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: preprocessing, 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 rideheight control with regard to comfort and efficiency.

A Databased Approach to Predict the Response Time of Cloudbased Vehicle Functions Future vehicular applications require sufficient computing and storage resources. Actual onboard resources are limited and inadequate to deal with steadily increasing requirements. In order to overcome the computational limitation in vehicles, computing intensive functions can be offloaded to a cloud. The socalled cloudbased vehicle functions run in the cloud and utilize cloud capability instead of vehicleu0027s onboard resources. The suitability of the vehicle functions for outsourcing to the cloud should be ensured in an early stage of development. Otherwise, shifting improper functions can lead to serious consequences for the vehicle control system. By the suitability analysis several criteria like functional safety, data dependency and response time should be considered. In this paper, we focus on the criterion response time and present a framework to predict the response time along a sample route.