A Cloud Big-Data-Driven Dynamics Control Approach for Unmanned Ground Vehicles for Safety Improving

Unmanned ground vehicles (UGVs) are important elements in the future intelligent transportation system (ITS), which is supposed to conduct various tasks for human beings. Obviously, the traditional edge-side (vehicle-side) dynamics control system strictly limits the performance improvement of multifunctional UGVs in various applications. Therefore, this article proposes a future ITS with multifunctional UGVs and a cloud-edge combined, big-data-driven dynamics control approach for UGVs to improve safety. The proposed cloud-edge combined control method is achieved based on big data machine learning optimization in the cloud-side controller and feedforward-feedback lateral dynamics control in the edge-side controller. In the cloud-side controller, the big data generated during the operation of the UGVs are stored and mined by the cloud brain control center to optimize the controller parameters of the edge-side controller to improve the dynamic performance and safety of the UGVs. Finally, the proposed cloud-edge combined controller is validated based on the experiments of a real UGV testbed.