EYE2DRIVE
The perfection of the human eye inspired EYE2DRIVE team
Eye2Drive’s Solutions for the Automotive Industry
In today’s landscape, imaging technology excels in various attributes such as speed, color fidelity, and high dynamic range (HDR). However, technology needs to improve adaptability to diverse lighting conditions, a particularly problematic constraint in automotive and AI-driven applications. While current solutions for the automotive market rely on adding redundant technologies like LIDAR and RADAR to improve safety, these additions increase complexity and cost.
EYE2DRIVE is introducing a new approach to imaging technology. Their unique intellectual property enables to create a dynamic imaging sensor that can adjust in real-time to the environment it’s in. This technology mimics the adaptability and resilience of biological vision, ensuring that the data captured is always of high quality and contextually relevant. Our solution provides a cost-effective yet robust alternative for a wide range of applications.
Problems we solve
Ghosting
Ghosting in vision systems refers to the appearance of faint duplicate images offset from their original positions. This issue typically arises from motion between the sensor and the subject or from processing artifacts. Ghosting can interfere with high-precision image analysis, making it a concern in applications like autonomous driving. Conventional solutions often involve both hardware and software optimizations.
Flickering
Flickering in autonomous vehicle vision systems refers to rapid variations in image brightness, often due to inconsistent lighting or sensor limitations. Flickering can impair the navigation system’s data interpretation and affect features like lane detection and object recognition. Traditional solutions typically involve hardware and software enhancements to stabilize image capture. Dynamic sensors are immune from flickering.
Exposure
Exposure challenges in automotive vision systems stem from rapidly changing lighting conditions. Key issues include handling high-contrast scenarios like tunnels, coping with glare from sunlight or headlights, managing low-light conditions, and adapting to quick transitions between bright and shaded areas. These challenges can compromise image quality, affecting the system’s ability to make accurate decisions.
