Montreal-based Algolux, a provider of machine learning optimization platforms for autonomous vision, has won the top award at the 2018 Detroit Automotive Tech.AD Conference.
Algolux was recognized for Most Innovative Autonomous Driving Solution for its CANA full-stack deep neural network (DNN).
“We are honored to win the Automotive Tech.AD Award for the Most Innovative Autonomous Driving Solution for our CANA robust perception DNN stack. This acknowledgment from industry experts once again validates our novel application of artificial intelligence as a unique approach to address the challenges of accurate perception under the most difficult imaging conditions,” said Allan Benchetrit, Algolux president and CEO. “The award highlights that Algolux’s AI technology for autonomous vision can tackle the mission-critical requirement of safe and robust perception for autonomous vehicles and ADAS.”
Its CANA full-perception stack applies an end-to-end DNN to improve accuracy for perceiving pedestrians, objects, and surroundings in harsh imaging conditions, such as low light and adverse weather. Its modular architecture can also be integrated with third-party perception systems and a range of power profiles.
“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 2018 Automotive Tech.AD Detroit Award ceremony,” said Sarah Farley, director of Smart Mobility & Automotive at we.CONECT, organizers of the AutomotiveTech.AD Conferences. “We congratulate Algolux on this acknowledgement by the autonomous vehicle community on the impact CANA can provide to improve the accuracy of perception systems and further progress the safety of these vehicles.”
