The mobility industry is rapidly advancing towards more autonomous modes of transportation with the adoption of sophisticated self-driving technologies. However, a critical challenge remains: the lack of standardized norms for defining, measuring, and ensuring vehicle visibility across various dynamic traffic environments. This lack of awareness is currently hindering the development of new safety regulations and the controlled transition to a fully integrated autonomous future.
While current efforts focus heavily on improving sensing technologies like computer vision, LiDAR systems, and sensor fusion, two key issues remain unresolved:
- The Dimensionality Gap: The absence of a representative and realistic three-dimensional color visibility model for measuring and comparing the visibility of complex shapes with large but varying color-coated surface areas.
- The Styling-Safety Paradox: The urgent need for enhanced visibility solutions that improve detectability for all traffic participants while maintaining absolute color styling freedom for brands.
A New Model for 3D Point-of-View (PoV) Visibility
To address these gaps, we are proud to reference a groundbreaking assessment model recently published by SAE International. This model measures the 3D Point-of-View (PoV) Visibility of mobility coatings using a 5-parameter Visibility Label.
Instead of measuring flat panels in a vacuum, this model evaluates how visible a coating color is on a complex 3D vehicle shape from the perspective of a separate observer—whether that observer is a human driver or an autonomous sensing device.
Key Components of the 3D PoV Model:
- Beyond Flat Panels: Moving to a comprehensive 3D visibility model that accounts for the curves and angles of modern vehicle design.
- Multimodal Evaluation: Considering visibility across human vision, computer vision, and LiDAR modalities.
- Standardized Baseline: Establishing Automotive Glossy Solid White as the global reference color for visibility comparison.
Data-Driven Safety
The research demonstrates that similar-looking colors can have significantly different 3D PoV Color Visibility scores. However, visibility is a tunable variable. With Crystal Glass Pigment (CGP) formulations, we can significantly improve visibility without altering the car’s intended aesthetic.
The technical data (as seen in Appendix B of the SAE publication) demonstrates staggering improvements when adding CGP to an existing silvery metallic automotive refinish color:
- Human Visibility: 200% increase (with 5 wt% CGP).
- Full Object Visibility: 600% increase (with 5 wt% CGP).
- LiDAR Visibility: 183% increase (with 17 wt% CGP).
Driving the Future of Road Safety
By emphasizing the importance of 3D PoV Color Visibility in environments where human-controlled and autonomous vehicles coexist, we aim to impact road safety today while facilitating a controlled transition to the future.
For a deep dive into the technical methodology and the 5-parameter label, we encourage stakeholders to review the full peer-reviewed article: Read the full article on SAE Mobilus
Reference: Mijnen, P., and Moerenburg, J., “A 3D Visibility Label for Mobility Coatings: Enhancing Traffic Safety Through Color and Sensor Perception,” SAE Int. J. CAV 9(2):1-42, 2026.
