Exploring the Role of Transformer Models in Vision, Multi-Modal, and Orbital Data for Autonomous Driving

Abstract

Abstract. Autonomous driving aims to reduce human error in driving, improve traffic efficiency, and provide a more comfortable driving experience. The integration of computer vision, advanced sensors, and machine learning has been pivotal in this advancement. The introduction of Transformer models has particularly revolutionized the field by offering a novel approach to processing data through attention mechanisms, which is crucial for tasks involving complex relationships between data elements. The paper categorizes research into three main approaches based on input data types: camera-based perception, multi-modal data fusion, and orbital data integration. As autonomous driving technology progresses towards higher levels of autonomy, with L2+ systems becoming standard, challenges remain in accurately interpreting complex environments, handling edge cases, and navigating legal and regulatory landscapes. The paper concludes that while Artificial Intelligence (AI) and deep learning advancements have brought autonomous driving closer to full realization, further research is necessary to address current limitations and ensure safe and reliable autonomous vehicle operation.