  Recently, realistic data augmentation using neural networks especially
generative neural networks (GAN) has achieved outstanding results. The
communities main research focus is visual image processing. However, automotive
cars and robots are equipped with a large suite of sensors to achieve a high
redundancy. In addition to others, ultrasonic sensors are often used due to
their low-costs and reliable near field distance measuring capabilities. Hence,
Pattern recognition needs to be applied to ultrasonic signals as well. Machine
Learning requires extensive data sets and those measurements are
time-consuming, expensive and not flexible to hardware and environmental
changes. On the other hand, there exists no method to simulate those signals
deterministically. We present a novel approach for synthetic ultrasonic signal
simulation using conditional GANs (cGANs). For the best of our knowledge, we
present the first realistic data augmentation for automotive ultrasonics. The
performance of cGANs allows us to bring the realistic environment simulation to
a new level. By using setup and environmental parameters as condition, the
proposed approach is flexible to external influences. Due to the low complexity
and time effort for data generation, we outperform other simulation algorithms,
such as finite element method. We verify the outstanding accuracy and realism
of our method by applying a detailed statistical analysis and comparing the
generated data to an extensive amount of measured signals.
