  High performance ultrasonic sensor hardware is mainly used in medical
applications. Although, the development in automotive scenarios is towards
autonomous driving, the ultrasonic sensor hardware still stays low-cost and
low-performance, respectively. To overcome the strict hardware limitations, we
propose to use capsule neural networks. By the high classification capability
of this network architecture, we can achieve outstanding results for performing
a detailed height analysis of detected objects. We apply a novel resorting and
reshaping method to feed the neural network with ultrasonic data. This
increases classification performance and computation speed. We tested the
approach under different environmental conditions to verify that the proposed
method is working independent of external parameters that is needed for
autonomous driving.
