In today’s landscape, imaging technology excels in various attributes such as speed, color fidelity, and high dynamic range (HDR). However, it falls short in adaptability to diverse lighting conditions, a constraint particularly problematic in automotive and AI-driven applications. While current solutions rely on adding redundant technologies like LIDAR and RADAR to improve safety, these additions increase both complexity and cost.
EYE2DRIVE proposes a fundamentally different approach. Drawing on our unique intellectual property, we are introducing a dynamic imaging sensor designed to adapt in real-time to environmental conditions. This technology mimics the resilience and adaptability of biological vision, ensuring consistently high-quality and contextually relevant data capture. Our solution aims to provide a robust yet cost-effective alternative for a wide range of applications.
Ghosting, Flickering, and Exposure in Autonomous Vehicles
Vision systems in autonomous vehicles are crucial for safe and efficient navigation. However, they face several challenges that can compromise their effectiveness. Three of the most common issues are ghosting, flickering, and exposure problems. Each of these issues can impact the system’s ability to accurately interpret and respond to its surroundings. Let’s delve deeper into each of these challenges.
Ghosting in vision systems refers to the appearance of faint duplicate images offset from their original positions. This issue typically arises due to motion between the sensor and 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 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 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.
Imaging Sensor Challenges
In the rapidly evolving world of autonomous vehicles, the ability to accurately interpret the environment is paramount. Traditional imaging systems, while effective under certain conditions, often falter when faced with challenging scenarios. From the complexities of HDR imaging to the intricacies of LED signals, the demand for more advanced vision systems is evident. Adaptive imaging devices, powered by Artificial Intelligence, are emerging as the solution to these challenges. These devices can dynamically adjust their sensitivity and processing capabilities to ensure high-quality image acquisition, even under the most demanding conditions.
When faced with challenging conditions, conventional imaging devices acquire multiple images at different exposures, and combine them in a single HDR image. This process can generate ghosting artifacts, making interpretation difficult. Adaptive imaging devices are intrinsically immune from this issue.
On a very sunny day, capturing the road ahead and the surrounding area of an autonomous vehicle can be difficult for a traditional imaging device. Under these conditions, only adaptive imaging devices that leverage intelligent imaging acquisition can performand meet the more stringent requirements.
A foggy environment can make it hard for conventional vision devices and systems to detect other vehicles and adequately identify road signals and lanes. Only new adaptive imaging devices can have their sensitivity dynamically adjusted to extract the highest quality and most critical information from the scene.
Heavy rain, in particular, can make it very difficult for the vision system of driverless cars to make the best decisions to ensure the vehicle’s and its passengers’ safety. The new dynamic imaging devices can handle most of those issues, supporting the navigation system even under challenging conditions.
Traditional vision systems can find it challenging to process situations with strong contrast between areas illuminated by the sun and those in shadow. Adaptive imaging devices can handle these situations without switching to HDR and the well-known ghosting issues it will bring.
As with the human eye, entering or exiting a tunnel can temporarily blind a conventional vision device,negatively impacting the decision process of the navigational system. Dynamic imaging sensors can adapt to drastic changes in light conditions in real-time.
Trafficated areas with multiple vehicles, visual signals, and light reflections can pose additional challenges to traditional vision devices. Only new-generation imaging sensors can handle the scene’s complexity, feeding the navigational system with high-quality images.
It is imperative for autonomous vehicles to identify all pedestrians on the scene, even in the most challenging conditions, to ensure their safety and the safety of the car passengers. Conventional imaging systems can struggle to capture the scene adequately.
Traffic lights, and in particular LED traffic lights, can be challenging for traditional vision systems. Factors like their position, flickering light intensity, direct sunlight, and reflections can throw these systems off.Dynamic imaging sensor technology can more efficiently manage these challenging conditions.
As LED headlights become more common, traditional vision systems face new challenges. The type and shape of lights, flickering, and reflections can easily confuse conventional vision systems. New imaging sensors, driven by AI, can adapt in real-time to these conditions and capture high-quality images.
LED Rear Lights
Just as headlights present unique challenges for conventional vision systems, LED rear-lights also introduce their complexities and challenges for the sensors. Only new dynamic vision technologies allow for read-lights accurate detection and identification on all road conditions.
Autonomous vehicles must detect and identify all road signals on the road. including LED signals, in order to stay within speed limits and be alerted of unpredictable road conditions. Adaptive and dynamic imaging sensors, driven by Artificial Intelligence, are the only ones up to the task.