29th January 2019
New Training Model Helps Autonomous Cars See AI’s Blind Spots
Since their introduction several years ago, autonomous vehicles have slowly been making their way onto the road in greater and greater numbers, but the public remains wary of them despite the undeniable safety advantages they offer the public.
Autonomous vehicle companies are fully aware of the public’s skepticism. Every crash makes it more difficult to gain public trust and the fear is that if companies do not manage the autonomous vehicle roll-out properly, the backlash might close the door on self-driving car technology the way the Three Mile Island accident shut down the growth of nuclear power plants in the United States in the 1970’s.
Making autonomous vehicles safer than they already are means identifying those cases that programmers might never have thought of and that the AI will fail to respond to appropriately, but that a human driver will understand intuitively as a potentially dangerous situation. New research from a joint effort by MIT and Microsoft may help to bridge this gap between machine learning and human intuition to produce the safest autonomous vehicles yet.
Reassuring a Wary Public
Were public hesitancy not a factor, every car on the road would be replaced with an autonomous vehicle within a couple of years. Every truck would be fully autonomous by now and there would be no Uber or Lyft drivers, only shuttle cabs that you would order by phone and it would pull up smoothly to the curb in a couple of minutes without a driver in sight.
Accidents would happen and people would still die as a result, but by some estimates, 90% of traffic fatalities around the world could be prevented with autonomous vehicles. Autonomous cars may need to recharge, but they don’t need to sleep, take breaks, and they are single-mindedly concerned with carrying out the instructions in their programming.
For companies that rely on transportation to move goods and people from point A to point B, replacing drivers with self-driving cars saves on labor, insurance , and other ancillary costs that come with having a large human workforce.
The cost savings and the safety gains are simply too great to keep humans on the road and behind the wheel.