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High performance ultrasonic sensor hardware is mainly used in medicalapplications. Although, the development in automotive scenarios is towardsautonomous driving, the ultrasonic sensor hardware still stays low-cost andlow-performance, respectively. To overcome the strict hardware limitations, wepropose to use capsule neural networks. By the high classification capabilityof this network architecture, we can achieve outstanding results for performinga detailed height analysis of detected objects. We apply a novel resorting andreshaping method to feed the neural network with ultrasonic data. Thisincreases classification performance and computation speed. We tested theapproach under different environmental conditions to verify that the proposedmethod is working independent of external parameters that is needed forautonomous driving.

Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors