Abstract
The development of autonomous driving systems heavily relies on high-quality LiDAR data, which is essential for robust object detection and scene understanding. Nevertheless, obtaining a substantial amount of such data for effective training and evaluation of autonomous driving algorithms is a major challenge. To overcome this limitation, recent studies are taking advantage of advancements in realistic simulation engines, such as CARLA, which have provided a breakthrough in generating synthetic LiDAR data that closely resembles realworld scenarios. However, these data are far from being identical to real data. In this study, we address the domain gap between real LiDAR data and synthetic data. We train deep-learning models for object detection using real data. Then, those models are rigorously evaluated using synthetic data generated in CARLA. By quantifying the discrepancies between the model’s performance on real and synthetic data, the present study shows that there is indeed a domain gap between the two types of data and does not affect equal to different model architectures. Finally, we propose a method for synthetic data processing to reduce this domain gap. This research contributes to enhancing the use of synthetic data for autonomous driving systems.
Aitor Iglesias
PhD Student
My research focuses on enhancing the reliability of Autonomous Driving functions using Machine Learning techniques.
Nerea Aranjuelo Ansa
Former PhD student
My research focuses on machine learning and computer vision for multimodal perception systems.
Ignacio Arganda-Carreras
Ikerbasque Research Associate Professor
My research interests include image processing, computer vision, and deep learning for biomedical applications.