Enhancing Traffic Safety Through Color and Sensor Perception
As the transportation and mobility industries transition to autonomous vehicles, a core challenge remains: reliably measuring and ensuring vehicle visibility across dynamic traffic environments. Despite advances in computer vision and LiDAR systems, a lack of universal standards for visibility, particularly regarding color and the complexities of three-dimensional vehicles, limits regulatory progress and leaves safety risks unresolved. Traditional approaches, such as lights and reflectors, focus on specific visibility points but overlook the bigger goal: the full surface and form detectability of complex vehicle shapes.
The Role of Color and Shape in Visibility
Visibility, defined as a vehicle’s capacity to be effectively detected by human and machine observers, is linked not only to technological equipment but also, fundamentally, to the object’s color and shape. Studies involving more than 850,000 traffic incidents¹ show a statistically significant association between car color and crash risk, with white cars having the highest visibility and the lowest crash rates. However, industry data reveals that color classifications are often too broad, overlooking important distinctions within color groups and the impact of shape on visibility under low-light conditions.
Vehicle shape adds to this complexity. Most visible surfaces are curved, causing angular-dependent point-of-view (AIPOV) visibility, in which colored coatings reflect and scatter light differently depending on the viewing angle. Empirical studies confirm that LiDAR reflectivity sharply decreases at higher incident angles, highlighting the importance of considering both object geometry and coating color composition in visibility models.
The 3D Point-of-View Visibility Model
To bridge these gaps, a 3D PoV color visibility model was developed³. The model simplifies real-world vehicle shapes into a representative shape and quantifiable semi-spherical geometry. A small part of the object is perpendicular to the observer, while 99% is curved away. This geometrical shape enables direct measurement and calculation of visibility from any observer’s point of view (human, camera, or LiDAR). Instead of relying solely on flat-panel perpendicular color tests, the model divides the semi-sphere’s observed area into five concentric rings of equal width, corresponding to different surface angles on the semi-sphere.
The 3D Point-of-View Visibility Model
To bridge these gaps, a 3D PoV color visibility model was developed³. The model simplifies real-world vehicle shapes into a representative shape and quantifiable semi-spherical geometry. A small part of the object is perpendicular to the observer, while 99% is curved away. This geometrical shape enables direct measurement and calculation of visibility from any observer’s point of view (human, camera, or LiDAR). Instead of relying solely on flat-panel perpendicular color tests, the model divides the semi-sphere’s observed area into five concentric rings of equal width, corresponding to different surface angles on the semi-sphere.
Visibility data is collected at five tangential angles (6°, 18°, 30°, 44° and 64°) using controlled lighting setups and standard measurement protocols. For each ring, reflectivity and luminance are measured, weighted by ring-surface area, and aggregated. This approach captures both the variation in color visibility due to angle and the contribution of the surface areas at larger angles, which are critical for understanding the environment under low-light conditions.
Measuring Modalities: Human Vision, Computer Vision and LiDAR
The model encompasses three major detection modalities:
- Human Vision: Assessed with Adrian’s Model (CIE), using relative luminance and contrast sensitivity, under two standardized lighting conditions (100 lux – dark overcast day, and 3 lux – end of civil twilight).
- Computer Vision: RGB average values measured by the camera, capturing visibility without subjective human color sensitivity.
- LiDAR Reflectivity: Intensity values, capturing sensor response irrespective of visible light conditions.
A mobility coating is characterized by its performance across these modalities, accounting for lighting conditions and angular dependence.
The 3D Color Visibility Label and Parameters
The proposed visibility label comprises five independent parameters:
- Human visibility (100 lux)
- Computer vision (average of 100 and 3 lux)
- LiDAR visibility
- Full object visibility (average human visibility of three outer rings, representing 84% of surface area, at an average of 100 and 3 lux)
- Twilight visibility (average human visibility of four outer rings, representing 96% of surface area, at 3 lux)
The Roadmap to 2027
This publication underscores that visibility is no longer a “nice-to-have”—it is a regulatory and safety necessity for ISO 21448 (SOTIF) compliance. By using Crystal Glass Pigment (CGP) technology, as showcased in the RheoLight™ case studies, formulators can achieve a “Twin-Flop” effect that provides both the shimmer of a premium metallic and the 360-degree safety of a high-visibility target.
