Cloud-based suspension control
Current suspension control systems are reactive, meaning that the active or semi-active suspension only reacts to road excitation. Some recent approaches use preview information from camera and radar to prepare the suspension in advance. In this work, we assume that such precise information is not available. Instead, improvements with respect to the reactive case are achieved using maps of the road roughness, available on the cloud. These maps are progressively learned and updated over time crowdsourcing measured data from different vehicles. The focus of this research is on the optimal use of this partial knowledge of the road excitation by the suspension controller, assuming different control techniques, accuracy level in the maps, on-board computational power.