Autonomous driving vehicles were supposed to be here by 2020. But COVID-19 and the sheer complexity of the challenge of anticipating what other drivers and pedestrians will do has delayed the release of fully autonomous driving vehicles.
The solution is that these vehicles need to drive more miles in test mode and log more data so that they know what to do in unexpected situations.
Ultimately, the training needed for AI models depends on data. Huge amounts of data. And all of this data is presenting a tremendous problem, because having the correct data to train the AI model for all possible scenarios is critical.
These issues are some of the reasons that Gartner, a global research and advisory firm, placed autonomous vehicles in the 2019 Trough of Disillusionment for their yearly Hype Cycle.
David Arnette is a Sr. Technical Marketing Engineer focused on Netapp infrastructure solutions for Artificial Intelligence and Machine Learning. He has published numerous reference architectures for enterprise applications on the FlexPod converged infrastructure and has over 18 years’ experience designing and implementing datacenter storage and virtualization solutions.