Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCVE45908.2019.8964864 | 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) |
Keywords | Field | DocType |
intelligent vehicles,big data applications,vehicle dynamics,suspensions | Obstacle,Data processing,Air suspension,Computer science,Ride height,Real-time computing,Vehicle dynamics,Cluster analysis,Big data,Cloud computing | Conference |
ISSN | ISBN | Citations |
2378-1289 | 978-1-7281-0143-9 | 0 |
PageRank | References | Authors |
0.34 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantin Riedl | 1 | 0 | 0.34 |
| Thomas Einmüller | 2 | 0 | 0.34 |
| Andreas Noll | 3 | 0 | 0.34 |
| Andreas Allgayer | 4 | 0 | 0.34 |
| David Reitze | 5 | 0 | 0.34 |
| Markus Lienkamp | 6 | 32 | 24.40 |