EYE2DRIVE
WE DEVELOP NEXT-GEN
IMAGE SENSORS FOR
AUTONOMOUS NAVIGATION
The perfection of the human eye inspired Monica Vatteroni, PhD, to design a silicon-based solution for digital imaging.
The Eye2Drive team created a next-generation CMOS imaging sensor leveraging our patented technology. It enables intelligent imaging acquisition for multiple industrial applications, including autonomous navigation and robotic.
We Design and Develop AI-Ready Imaging Sensors

We design and develop advanced image-sensing hardware technology focused on achieving the highest performance and precision. Our Eye2Drive silicon CMOS imaging sensors are meticulously crafted, fully integrated, and uniquely optimized for AI-driven applications. These sensors are protected by several core patents, which safeguard our innovative technology. With artificial intelligence powering our sensors, they can dynamically adjust and adapt to changing conditions in real time, providing exceptional accuracy and reliability.
A Technology Based on a Strong Patent Portfolio

Eye2Drive‘s cutting-edge technology is powered by a robust portfolio of proprietary patents developed and owned by the company. This solid intellectual property foundation underscores Eye2Drive’s exceptional technological expertise and innovation in advanced vision systems and sensors.
This comprehensive patent portfolio showcases the company’s advanced knowledge. It ensures a robust market position, providing clients with unmatched reliability and protection in autonomous navigation and imaging sensor landscape.
Eye2Drive’s Sensor is the Future of Autonomous Navigation
We did a SWOT analysis comparing the major sensor technologies for autonomous navigation vehicles: traditional imaging sensors, LiDars, Radars, and the new generation of bio-inspired sensors created by Eye2Drive. The results of our analysis are shared in the comparison table below.
Camera Sensors | LiDAR Sensors | Radar Sensors | Eye2Drive Sensors | |
---|---|---|---|---|
Technology | Captures images of the environment using CMOS sensors. | Emits laser beams to create a 3D map of the environment. | Emits radio waves to detect objects and measure their distance and speed. | Captures images using bio-inspired vision technology, mimicking the human eye’s ability to adapt to changing light conditions. |
SStrengths | High resolution, cost-effective, able to perceive color and texture. | High accuracy, provides detailed 3D maps, works well in low-light conditions. | Works in all weather conditions, long range, accurate speed and distance measurements. | HDR (High dynamic range), high sensitivity, low latency, no-flickering, no-ghosting, and full saturation control. |
WWeaknesses | Performance is affected by poor lighting, weather conditions, and limited depth perception. Acquired images can require complex post-processing. | High-cost, performance can be degraded by adverse weather conditions. | Lower resolution than cameras and LiDAR, limited ability to capture fine details. | Still, under development, it may be more expensive than a traditional camera sensors, but will wastly simplify the navigation system software. |
OOpportunities | Can be used in a variety of applications, including object recognition and classification, lane-keeping assist, and traffic sign recognition. | Can be used for mapping, object detection, and autonomous navigation. | Can be used for adaptive cruise control, blind spot detection, and collision avoidance. | Can be used for autonomous navigation, robotics, defense, and medical imaging. The ability to handle challenging conditions and provide high-quality images opens up new possibilities. |
TThreats | At risk of being replaced by newer technologies, such as bio-inspired imaging sensors. The increasing capabilities of other sensors could diminish their prominence in autonomous navigation. | Too expensive for some applications. Advancements in camera technology and bio-inspired sensors could challenge LiDAR’s position in the market. | Less accurate than other imaging sensors. In the future, more sophisticated sensor techniques could reduce the reliance on radar alone. | May take a long time to be widely adopted. The technology’s novelty means it must compete with established sensor solutions and overcome potential integration challenges. |
OUR TESTIMONIAL