Cloud-based vehicle ride-height control

Konstantin Riedl, Thomas Einmuller, Andreas Noll, Andreas Allgayer, David Reitze, Markus Lienkamp

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publication2019 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728101422
DOIs
StatePublished - Nov 2019
Event8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019 - Graz, Austria
Duration: 4 Nov 20198 Nov 2019

Publication series

Name2019 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019 - Proceedings

Conference

Conference8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019
Country/TerritoryAustria
CityGraz
Period4/11/198/11/19

Keywords

  • Big data applications
  • Intelligent vehicles
  • Suspensions
  • Vehicle dynamics

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