The Big Picture
What autonomous cars can teach us about driving
The moral maze
You’re rolling down the freeway in heavy, fastmoving traffic, following a truck. On your right is a new Volvo XC90 with a “Baby On Board” sticker in the window. On your left, a suburban outlaw in a $500 leather jacket blat-blat-blatting along on his Harley-davidson. Suddenly, a large, heavy object falls off the back of the truck, right in your path. There’s no chance of stopping. What do you do? Stay in your lane and brace for the head-on hit? Swerve left and take out the motorcyclist? Or dive to the right and ram the Volvo?
Chances are you’ll simply react, stomping the brake pedal and swinging the wheel one way or the other. You’ll only think of the consequences—the dead motorcyclist or the badly injured baby—when the shock wears off and your hands stop shaking and you’re lying in bed in the dark wondering if you’ll ever sleep again.
But what if you did have the ability to analyze the situation in real time as it unfolded in front of you and logically determine a course of action? What would you do? Prioritize your own safety by aiming for the motorcyclist, minimize the danger to others at the cost of your own life by not swerving, or take the middle ground and centerpunch the Volvo, hoping its high crash safety rating gives everyone a chance of survival?
Forget bustling city streets and complex freeway interchanges: Navigating a moral maze like this is the toughest task facing autonomous vehicles.
Patrick Lin is director of the Ethics + Emerging Sciences Group at California Polytechnic State University, San Luis Obispo, and he constructs grisly thought experiments like the one above to highlight the fundamental issue facing the deployment of autonomous vehicles on our roads. Autonomous vehicles have the potential to dramatically reduce the incidences of death and injury on our roads, ease congestion, and reduce emissions. The problem is not the capability of autonomous vehicle technology. It’s deciding how that capability should be used.
In the crash scenario outlined above, any consequent death would be regarded as the result of an instinctual panicked move on the part of the driver, with no forethought or malice. But what if that death was the result of behaviors programmed into an autonomous vehicle by an automaker’s software coder in San Jose or Shanghai? “That looks more like premeditated homicide,” Lin says bluntly. Why? Because optimizing an autonomous vehicle to ensure it minimizes harm to its occupants in such a situation—something we’d all want the one we’re riding in to do—involves targeting what it should hit.
A crash is a catastrophic event, but at its core is a simple calculation: Force equals mass times acceleration. Designing a vehicle that helps its occupants survive a crash is therefore fundamentally an exercise in slowing its rate of deceleration (the numerically negative form of acceleration) during the crash event, usually by engineering crumple zones around a strong central passenger cell.
Autonomous technology adds an active element to that calculus: When a collision is unavoidable, it has the potential to be able to direct the vehicle to hit the smallest and lightest of objects—the motorcycle rather than the Volvo, for example—to enhance the probability its occupants will survive. That outcome is the direct result of an algorithm, not instinct. So who bears responsibility for the death of the motorcyclist? The programmer who wrote the algorithm? The automaker that determined such an algorithm should be part of its autonomous vehicle’s specification? The person whose journey put the motorcyclist at risk?
Good questions. No easy answers. Ironically, the debate over ethics and autonomous vehicles highlights an uncomfortable truth that’s too often ignored when we mere humans climb behind the wheel: Cars can kill. It’s not just robots that should always drive like someone else’s life depended on it. n
What if you did have the ability to analyze the situation in real time as it unfolded in front of you? What would you do?