The proposed regulations preempt state regulation of vehicle design, and allow companies to apply for high volume exemptions from the standards that exist for human-driven cars. There is a new research area known as "explainable AI" which hopes to bridge this gap and make it possible to document and understand why machine learning systems operate as they do. The most interesting proposal in the prior document was a requirement for public sharing of incident and crash data so that all teams could learn from every problem any team encounters. The new document calls for a standard data format, and makes general motherhood calls for storing data in a crash, something everybody already does.
Nathan is a Reader in the Department of Computer Science at the University of Warwick, whose research into the application of machine learning for autonomous vehicles (or "driverless cars") has been supported by a Royal Society University Research Fellowship. My research uses machine learning to give insights into how objects or people interact and how patterns emerge and evolve. Machine learning algorithms will examine previous behaviours and learn from these behaviours, to then predict what will happen in the future. An accurate algorithm could then be used to inform the decisions vehicles make and predict vehicle journeys and routes.