Artificial IntelligenceAutonomous

Zenuity and CERN team up development of fast machine learning for autonomous drive cars

Zenuity, the autonomous driving software company is teaming up with CERN in the development of fast machine learning for autonomous drive cars. According to Zenuity, it is the first automotive company to partner the European Organization for Nuclear Research.

A fundamental challenge in the development of autonomous drive (AD) cars is the interpretation of the huge quantities of data generated by normal driving conditions, such as identifying pedestrians and vehicles with the sensors on the car, including cameras, lidar and radars. Addressing these issues is crucial for the development of safe AD cars.

Zenuity’s long-term ambition is to speed up the development of vehicles that will completely eliminate car collisions and associated injuries and fatalities. This collaboration with CERN will ultimately help Zenuity develop AD cars that can reach decisions and make predictions more quickly, thus avoiding accidents.

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 that 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.

The research to be conducted under 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 great success, even exceeding human performance on certain tasks.

Zenuity hopes that their collaboration with CERN will push the frontiers of this technology by reducing the runtime and memory footprint of the relevant deep learning algorithms without reducing accuracy, while simultaneously minimizing energy consumption and cost.

Source: Press Release


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