Challenges in training algorithms for autonomous cars

In my earlier article, we talked about the usage of machine learning algorithms in autonomous cars. Obviously, the process of learning and implementation of machine learning is not without a huge set of challenges. Attaining superior accuracy of detection and prediction: Safety-critical systems as used in self-driving cars, require detection accuracy much higher than in the internet industry. These systems are expected to operate flawlessly irrespective of weather conditions, visibility, or road surface quality. Challenge of scale: deep neural networks, such as those used in self-driving vehicles, require a mind-boggling amount of computational power.