How Can We Speed Up Autonomous Vehicle Deployment?
CEO of DriveU.auto, accelerating autonomous vehicle deployment through teleoperation. Getty  Autonomous vehicles (AVs)  which are already being deployed as robotaxis, trucks and delivery robots  are ushering in a revolution in the world of transportation. Not only do AVs have the potential to make driving safer by reducing human error, but it's estimated that wide-scale deployment of AVs will bring about an average 40% reduction in travel time. But building an AV is an extremely complex and capital-intensive endeavor, employing some of the greatest minds on the planet hard at work on this formidable task. I believe wide-scale autonomous vehicle deployment hinges on three pillars:   Regulatory framework.  A business case.  Technological maturity. The Autonomous Vehicle Regulatory Framework  Autonomous vehicles are a completely new entity to exist on public roads. As such, regulation needs to be created to address governing safety laws (e.g., until recently, safety laws in the U.S. required a steering wheel), licenses to conduct business, interactions with other entities on public roads, liability and more. In recent months, we've seen concrete steps toward establishing a regulatory framework  for example, proposed regulation in Germany and recommendations on liability in the U.K.  A Business Case For AVs  When most people think of autonomous vehicles, the first thing that springs to mind is robotaxis and private cars. The fact of the matter is that the majority of commercial AV implementations revolve around other use cases that offer a faster return on investment. Narrowly defined use cases designed to solve a specific pain point often offer a clear business case and are the path the industry is taking. Some examples include the delivery of food, yard operations and long-haul trucking. Achieving Technological Maturity With AI  AVs use artificial intelligence (AI) technology to "understand" different road scenarios and calculate the best options for dealing with them. Training the AI to successfully address different edge cases and scenarios is a huge challenge, as there's an almost infinite variety of these situations to deal with. For example, AI needs to learn how to tell the difference between a flock of birds and a pile of leaves to determine whether an AV should brake or accelerate. An AV's AI is trained using machine learning and neural networks. An AV learns how to respond by analyzing other, relevant experiences so that it knows what decision to make when it's confronted with a similar scenario for the first time. This means that if an AVs AI is trained correctly, it will already know how to respond to a large number of scenarios and edge cases, even if it hasnt actually experienced them in the past or in training.  The challenge is that there are simply too many potential scenarios to learn. Couple that with the fact that the AI needs to be retested for every version and model, and its easier to appreciate the scale of the challenge. Two Down, One To Go  Before AVs become a common sight, there are three things that need to happen. A regulatory framework needs to be put in place, viable business models that justify the billions invested need to be established and the AI that makes its decisions will need to go through quite a bit of training. Progress is being made along the first two pillars, which are very much in the control of the people driving the AV industry. The technological maturity has come a long way but still requires work to enable a viable, large-scale deployment. I'll attempt to answer this question in my next article on the picks and shovels of the AV space. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives.