Researchers Use Machine Learning in Self-Driving Cars to Avoid Crashes

In the journey to an autonomous driving future, one of the primary difficulties that remain is the capacity for cars to make rapid, potentially life-saving decisions. Innovators are still perfecting this element, and a new research project between CERN and car safety company Zenseact has been published investigating the use of deep learning algorithms to help cars avoid collisions. The technology relies on computer vision to analyze and respond to a car’s environment rapidly and accurately. "Deep learning has strongly reshaped computer vision in the last decade, and the accuracy of image-recognition applications is now at unprecedented levels,” said Christoffer Petersson, Zenseact research lead. “Results show great promise for future speed and accuracy increases in image recognition for autonomous vehicles, helping to improve cars’ ability to avoid accidents,” Zenseact said.