Object classification in the simulation environment using ultrasonic sensor arrays

With the wide application of machine learning technologies in advanced driver assistance systems of vehicles, object classification on obstacles has attracted much attention. Ultrasonic sensors are mainly used to measure the distance to obstacles under the condition of low-speed movement. The existing ultrasonic sensor is low-cost, and its good performance and robustness are sufficient for obstacle avoidance. Recent progress on ultrasonic has attempted to classify obstacles with the combination of ultrasonic sensors and machine learning. It shows that deep neural networks are able to classify objects using only ultrasonic sensors. In the thesis, we focus on the object classification on sizes of obstacles and expect our proposed neural networks model can solve the classification task under the simulation environment, thus contributing to the application of ultrasonic sensors in vehicles. The ultrasonic sensor arrays are built in COMSOL Multiphysics and can provide ultrasonic data with different objects. After many simulation experiments, the ultra-sonic data from objects are labeled and stored in datasets. Then we process the ultrasonic data from datasets and feed them to the proposed neural networks. The ultrasonic data obtained by experiments are examined by the distribution of reflected ultrasonic rays in simulation. The analysis results are in line with our expectations. The trained neural networks are divided into two groups. The first networks group is trained with data from cubes and shows over 80% accuracy on five object categories. The second group of networks is trained with data from cubes and S1 objects. There is an approximate 5% drop in classification performance as the difficulty of the classification task increases.