Zenuity, CERN team up on machine learning for autonomous driving

Zenuity, CERN team up on machine learning for autonomous driving

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By Christoph Hammerschmidt

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, and Zenuity regards this as a key part of its own raison d’être of developing software for vehicles that will eliminate car collisions and associated injuries and fatalities.

Zenuity’s management associates the step with the hope that the collaboration with CERN will ultimately help the company develop AD cars that can reach decisions and make predictions more quickly, thus avoiding accidents.

CERN’s actual task – 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) – seems to have has little to do with Zenutity’s business area. A closer look, however, reveals one thing in common: 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 microseconds. 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 deep learning, 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.


More information:

Volvo, Autoliv to build Nvidia-based automated driving platform

Autoliv, MIT join forces in autonomous vehicle research


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