Autonomous driving software company, Zenuity is cooperating with CERN, the European Organisation for Nuclear Research, to develop fast machine learning for self-drive cars.
A fundamental challenge in development of autonomous drive (AD) cars is interpretation of huge quantities of data generated by normal driving conditions, such as identifying pedestrians and vehicles with sensors on the car, including cameras, lidar and radars.
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Addressing these issues is crucial for the development of safe AD cars and is part of Zenuity’s ambition to speed up development of vehicles, which will completely eliminate car collisions and associated injuries and fatalities.
One of the main quests at CERN is to study the standard model of particle physics by collecting large quantities of data originating from particle collisions produced by CERN’s Large Hadron Collider (LHC). Both particle physics and autonomous vehicles require fast decisions to be made.
CERN has approached this challenge by using Field-Programmable Gate Arrays (FPGAs), a hardware solution which can execute complex decision-taking algorithms in micro-seconds.
The synergy between Zenuity and CERN aims to use FPGAs for fast Machine Learning applications, to be used in the AD industry and in particle physics experiments.
“I think it says something important about the collaborative nature of science, an organisation like CERN that conducts high-energy particle collisions can work with a company that is dedicated to completely eliminating collisions… in traffic,” said Zenuity chief executive, Dennis Nobelius.
The research to be conducted in the collaboration concerns so-called deep learning, which is a class of machine learning algorithms.
In recent years such algorithms, commonly referred to as AI, have been applied to a multitude of fields with success, even exceeding human performance on certain tasks.
