Abstract
Autonomous driving is no longer a topic of science fiction. Advancements of autonomous driving technologies are now reliable. Effectively harnessing the information is essential for enhancing the safety, reliability, and efficiency of autonomous vehicles. In this article, we explore the pivotal role of visualization and visual analytics (VA) techniques used in autonomous driving. By employing sophisticated data visualization methods, VA, researchers, and practitioners transform intricate datasets into intuitive visual representations, providing valuable insights for decision-making processes. This article delves into various visualization approaches, including spatial-temporal mapping, interactive dashboards, and machine learning-driven analytics, tailored specifically for autonomous driving scenarios. Furthermore, it investigates the integration of real-time sensor data, sensor coordination with VA, and machine learning algorithms to create comprehensive visualizations. This research advocates for the pivotal role of visualization and VA in shaping the future of autonomous driving systems, fostering innovation, and ensuring the safe integration of self-driving vehicles.
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