We Need Smarter Driverless Vehicle Regulations More Urgently than We Need Smarter AI

By Steven E. Shladover

Excitement about artificial intelligence (AI) has inflated expectations about what machine learning technology could do for automated driving. There are fundamental differences between AI’s large language models (LLMs) manipulating words into sentences, however, and machines driving vehicles on public roads. Automated driving has safety-of-life implications not only for the passengers of driverless vehicles but also for everybody else who shares the road. Its software must be held to much higher standards of accuracy and dependability than LLMs that support desktop or mobile phone apps.

Although well-justified concerns surround human driving errors, the frequency of serious traffic crashes in the U.S. is already remarkably low. Based on the traffic statistics from the National Highway Traffic Safety Administration (NHTSA), fatal crashes occur approximately once in every 3.6 million hours of driving and injury-causing crashes about once in every 61,000 hours of driving. That’s one fatal crash in 411 years and one injury-causing crash in seven years of continuous 24/7 driving. Comparably long mean times between failures are extremely difficult to achieve for complex software-powered systems, particularly ones mass-produced at affordable prices.

Driverless vehicle company Cruise’s problems with California’s safety regulators and Tesla’s problems with NHTSA indicate some of the safety challenges that automated driving software systems face. They are more than purely technological because they also demonstrate the serious risks associated with both companies’ attempts to bring the Silicon Valley culture of “moving fast and breaking things” into an application where safety needs to be the top priority. Developing safe systems requires patience and meticulous attention to detail, both of which are incompatible with speed. And our vehicles should not be breaking things—especially people.

That’s why the U.S. needs a rigorous safety regulatory framework for automated driving—so that the safety-enhancing potential of the technology can be realized and public trust in its safety can be earned by the industry, once it is properly vetted by safety experts and safety regulators. Because of its safety-critical…