Impact detection using piezoelectric sensors is an actual and widespread research field. The current work provides an approach for a real-time realization of an impact detection system using deep learning methods. For realization a hardware software co-design approach is used utilizing hardware acceleration by a continuous pipelining FPGA structure. The concept describes the hardware software partitioning of the underlying functions and the methodology for ensuring continuous data processing and the associated real-time capability. The behavior of the hardware is realized with the help of a finite state machine and thus the correctness of the data is ensured and the impact identification is realized. The results show the real-time capability as well as a reasonable resource utilization of the FPGA design.

The required energy savings can be achieved in all automotive domains through weight savings and the merging of manufacturing processes in production. This fact is taken into account through functional integration in lightweight materials and manufacturing in a process close to large-scale production. In previous work, separate steps of a process chain for manufacturing a center console cover utilizing a sensoric hybrid laminate have been developed and evaluated. This includes the process steps of joining, forming and inline polarization as well as connecting to an embedded system. This work continues the research process by evaluating impact localization methods to use the center console as a haptic input device. For this purpose, different deep learning methods are derived from the state of the art and analyzed for their applicability in two consecutive studies. The results show that MLPs, LSTMs, GRUs and CNNs are suitable to localize impacts on the novel laminate with high localization rates of up to 99%, and thus the usability of the developed laminate as a haptic input device has been proven.