Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks

Abstract

Generative Adversarial Networks (GANs) have achieved outstanding results in generation of realistic data, particularly for image data. Autonomous driving systems are equipped with a large suite of sensors to obtain robustness and redundancy. Ultrasonic sensors are commonly used because of their low-cost and reliability of near-field distance estimation. However, machine learning algorithms are not commonly used for ultrasonic data, as it requires extensive datasets whose creation is time-consuming, expensive and inflexible to hardware and environmental changes. On the other hand, there exists no method to simulate these signals deterministically. Thus, we present a novel approach for synthetic ultrasonic signal simulation using conditional GANs (cGANs). To the best of our knowledge, we present the first realistic data augmentation for automotive ultrasonics sensors. The performance of cGANs allows us to bring the environment simulation to a high quality close to realistic data. By using our setup and environmental parameters as condition, the proposed approach is flexible to external influences. Due to its low complexity and smaller time effort needed for data generation, the proposed method outperforms 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.