Algolux’s Eos Embedded Perception Software leverages deep learning to uniquely address the robustness and scalability limitations of today’s vision system architectures. This enables a massive improvement in robustness for all conditions, both good and challenging, and allows the stack to be quickly tailored to any camera lens/sensor combination vs. state-of-the-art.
“Algolux was identified as a finalist from a large pool of nominees providing autonomous vehicle technologies and was presented with the first-place award at the Tech.AD Europe Award 2020 ceremony,” said Daniel Wolter, CEO & Co-Founder of we.CONECT, organizers of the Automotive Tech.AD Conferences. “We congratulate Algolux on this acknowledgment by the automotive community on the impact Eos can provide to improve the accuracy of perception systems and further increase the safety of vehicles.”
“We are honored to win the Automotive Tech.AD Award for the Most Innovative Use of Artificial Intelligence & Machine Learning in the Development of Autonomous Vehicles & Respective Technologies for our Eos Embedded Perception software. This acknowledgment from industry experts once again validates that our novel application of artificial intelligence addresses the fundamental challenge of accurate perception under the most difficult operating conditions,” said Allan Benchetrit, Algolux President and CEO. “The award highlights the amazing work from our team and further proves that Algolux’s computer vision technology can tackle the mission-critical requirement of safe and robust perception for ADAS and autonomous vehicles.”
Algolux’s award-winning AI technologies enable highly robust vision systems. We help vision systems perceive better than any alternative, especially in harsh conditions such as low light, adverse weather, and dusty environments. Computer vision is at the heart of ADAS and autonomous cars and is leading the next wave of market growth and societal impact. Developed by an industry-recognized research team, our patented deep learning technologies address the complexity of optimizing imaging and vision systems to improve vehicle safety while reducing cost, time-to-market, and scalability risks.