There’s more to the development of driverless cars than the work that computer scientists and engineers are doing to make them perceive where they are and figure out how to get from Point A to Point B.
“Most of robotics is focused on how to get robots to achieve the task, and obviously this is really, really important,” said Anca Dragan, a roboticist at Berkeley, and the head of the Interactive Autonomy and Collaborative Technologies lab. “But what we are doing in my lab, is we focus on how these algorithms need to change when robots are actually out there in the real world. How they need to coexist with people, direct people, and so on.”
In other words, robots are learning to tailor their behaviors to the presence of humans. Which is difficult, even for humans! Because that kind of customization is based on a vast overlay of experience and guesswork.
“How does the robot decide which actions to take?” Dragan said. “How do you figure out the state of the world—when that world also contains people and their internal states, their plans, their intentions, and their beliefs about the robots?”
For starters, the robots rely on models of human behavior—which is based on approximations about how people tend to prize convenience and efficiency as they interact with their environment. Such models come from actual observation of human behavior, and might also factor in the fact that while humans prioritize getting somewhere quickly—they also routinely take action to avoid collisions, like moving out of the way if another car is veering into theirs.
“Much like the robot, you are also plotting what to do, and actively thinking about the road system and the actions you take,” Dragan told me. “By learning how humans act on a highway, the robot is indirectly learning how to behave itself.”
This sort of learning is precisely what might help solve the stalled-forever-at-a-four-way-stop dilemma. In one experiment, for example, Dragan and her colleagues taught an algorithm to observe human drivers in a highway setting—then tested to see how the algorithm would apply what it had just learned in other scenarios. At a four-way stop, it didn’t just sit there and wait for other cars to go first. To the surprise of the researchers, the robot figured out a way to signal its intentions to the human driver.
“Our robot does something really cool and a bit counterintuitive,” Dragan told me. “What it decides to do, is it decides to slightly back up a little. And by backing up a little, it prompts the person to go, because the robot is clearly not moving forward. So the danger of colliding with the robot is very, very low—compared even with the robot just sitting there.”
The lesson the robot learned from highway driving is that people often speed up when there is more space between their car and other vehicles. So, the robot figured, one way to encourage another car to move is to create a greater space between you and that car. “It was able to transfer the model of that [human behavior] to the four-way stop,” Dragan said. “On the highway, the robot wouldn’t back up. But in a four-way stop, the right thing to do was to back up.”