Convolutional neural network with data augmentation for object classification in automotive ultrasonic sensing

Abstract

Today's low-cost automotive ultrasonic sensors perform distance measurements of obstacles within the close range of vehicles. For future parking assist systems and autonomous driving applications, the performance of the sensors should be further increased. This paper examines the processing of sensor data for the classification of different object classes and traversability of obstacles using a single ultrasonic sensor. The acquisition of raw time signals, transformation into time-frequency images, and classification using machine learning methods are described. Stationary and dynamic measurements at a velocity of 0.5 m/s of various objects have been carried out in a semi-anechoic chamber and on an asphalt parking space. We propose a scalogram-based signal processing chain and a convolutional neural network, which outperforms a LeNet-5-like baseline. Additionally, several methods for offline and online data augmentation are presented and evaluated. It is shown that carefully selected augmentation methods are useful to train more robust models. Accuracies of 90.1% are achieved for the classification of seven object classes in the laboratory and 66.4% in the outdoor environment. Traversability is correctly classified at an accuracy of 96.4% and 91.5%, respectively.