An Approach for Combined Vertical Vehicle Model and Road Profile Identification from Heterogeneous Fleet Data In this work a novel approach for concurrent vehicle model parameter and road profile identification is proposed which exploits the availability of fleet data. By combining measurement data, obtained from lowcost smartphone sensors, for multiple vehicles, typical identifiability issues present in singlevehicle measurements can be circumvented. Moreover, as the presented approach exploits a low order model where the parameters are identified for a specific asset (i.e. a digital twin), the shared road profile can be identified, rather than just the resulting forces. A computational framework is presented and a first experimental validation is performed where a speedbump is identified using data from three different vehicles. This approach has the potential to improve the availability of accurate data for a.o. customer correlation durability design, road condition monitoring, and active suspension systems.

Multiactuated ground vehicle tyre force estimation through a coupled 1D simulationestimation framework This paper disseminates the use of a coupled 1D simulationestimation framework employed for multiactuated ground vehicle tyre force virtual sensing. The forces generated in the tyre contact patches govern the vehicle motion and behaviour on the road. Therefore, they are highly relevant for Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) technologies, especially when it comes to ensuring vehicle stability and driving safety in extreme manoeuvres. However, some ADAS and AD features require accurate and robust estimation of additional vehicle dynamics related quantities. Thus, this paper proposes a framework for the consistent use of 1D vehicle models of different complexity together with stateoftheart estimation techniques to enable joint state, disturbance, parameter (SDP) estimation.