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In recent decades, the field of autonomous driving technology has witnessed remarkable growth. With the support of artificial intelligence, self-driving cars are transitioning from concept to reality, offering immense potential for reducing traffic accidents and transportation costs. However, despite the vast amounts of data generated by autonomous vehicles, it remains underutilized, particularly in the areas of visualization and visual analytics.
Numerous studies have underscored the importance of visualization in diagnosing, evaluating, and interpreting autonomous driving models. The high-dimensional, multimodal, and spatiotemporal nature of autonomous driving data presents significant challenges in leveraging visualization to reveal underlying correlations and patterns. By addressing these challenges, we can provide invaluable insights to benefit developers in the autonomous driving field.
The aim of this special issue is to advance the knowledge and understanding of autonomous driving through visual analytics, paving the way for novel insights and developments in this rapidly evolving domain. We invite contributions that explore innovative methodologies and effective applications of visualization and visual analytics methods in autonomous driving research.
Topics of interest include, but not limited to:
For author information and guidelines on submission criteria, visit the Author's Information page. Please submit papers through the ScholarOne system and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by email to the guest editors directly.
Contact the guest editors at cga3-2024@computer.
