Google’s autonomous cars have already shown how close vehicles are to driving themselves in day-to-day traffic, but there’s still one uncontrollable, unpredictable, and often-irrational variable that autonomous cars still struggle to cope with: you, me and all the other haphazardly-programmed human beings on the road. And though predicting human behavior might be one of the most difficult tasks for a human-programmed computer, researchers at MIT are already digging into the challenge. Using model cars (one autonomous, one human-controlled) on overlapping tracks, 97 out of 100 laps avoided collision. But not all of those laps fell into the near-collision “capture set”… which, as it turns out, is what makes the human threat to autonomous cars so challenging.
According to [MIT Mechanical Engineering Professor Domitilla] Del Vecchio, a common challenge for ITS developers is designing a system that is safe without being overly conservative. It’s tempting to treat every vehicle on the road as an “agent that’s playing against you,” she says, and construct hypersensitive systems that consistently react to worst-case scenarios. But with this approach, Del Vecchio says, “you get a system that gives you warnings even when you don’t feel them as necessary. Then you would say, ‘Oh, this warning system doesn’t work,’ and you would neglect it all the time.”
That’s where predicting human behavior comes in. Many other researchers have worked on modeling patterns of human driving. Following their lead, Del Vecchio and Verma reasoned that driving actions fall into two main modes: braking and accelerating. Depending on which mode a driver is in at a given moment, there is a finite set of possible places the car could be in the future, whether a tenth of a second later or a full 10 seconds later. This set of possible positions, combined with predictive models of human behavior — when and where drivers slow down or speed up around an intersection, for example — all went into building the new algorithm.
The result is a program that is able to compute, for any two vehicles on the road nearing an intersection, a “capture set,” or a defined area in which two vehicles are in danger of colliding. The ITS-equipped car then engages in a sort of game-theoretic decision, in which it uses information from its onboard sensors as well as roadside and traffic-light sensors to try to predict what the other car will do, reacting accordingly to prevent a crash.
When both cars are ITS-equipped, the “game” becomes a cooperative one, with both cars communicating their positions and working together to avoid a collision.