Contrast Optimization for Camera-Based ADAS to Enhance Object Visibility Using Pixel Light Technology

Micro-LED pixel emitter arrays mark an important advancement in automotive lighting technology. Their exceptional resolution by means of high pixel counts enables precise and adaptive beam shaping, making them ideal for real-time optimization under varying traffic and environmental conditions. This functionality significantly enhances the capabilities of Adaptive Driving Beam (ADB) headlamps, allowing for glare-free high beams and improved safety. Moreover, their energy-efficient selective activation supports more sustainable lighting designs, and their potential to project images or symbols adds further value in terms of road communication. To fully leverage the benefits of pixel lighting technology, it is essential to understand how resolution, and pixel activation strategies influence visual contrast and object visibility. This work presents a physically based, large-scale simulation approach that systematically varies pixel densities and scene parameters such as distance, ambient luminance, and object reflectance. Over two billion combinations were analyzed to evaluate object detectability. Results reveal a strong non-linear relationship between pixel resolution and visibility performance. Visibility improves rapidly up to ~100,000 pixels, then saturates between 150,000-200,000 pixels, indicating a practical resolution threshold. Key influencing factors include object distance, pixel density, reflectance, and activation logic. These findings support a shift in headlamp design: from maximizing brightness to optimizing contrast through intelligent and resolution-aware light distribution. The presented framework offers a basis for future ADAS-oriented lighting development.