Autonomous driving (AD) technology has experienced unprecedented growth in recent years, propelled by advancements in artificial intelligence. The transition from theoretical concepts to tangible implementations of self-driving cars holds immense promise in revolutionizing transportation, with the potential to significantly reduce traffic accidents and associated costs. However, despite this rapid progress, the field still grapples with underutilization of the vast datasets generated by autonomous vehicles, particularly in the realm of visualization and visual analytics, or in a broader sense, visual computing.