Engineers at the University of California, Berkeley, are preparing autonomous cars to predict what humans might do next. A team has developed an algorithm that can guess with up to 92% accuracy whether a human driver will make a lane change.
Enthusiasts believe that self-driving vehicles could lead to fewer crashes and less traffic. But people aren’t accustomed to driving alongside machines. When we drive, we watch for little signs from other cars to indicate whether they might turn or change lanes or slow down. A robot might not have any of the same tics, and that could throw us off.
Volunteers were asked to drive in a simulator. Each time the driver decided to make a lane change, they pushed a button on the steering wheel before doing so. The researchers could then analyse data from the simulator for patterns at the time of lane changes: where were all of the cars on the road? How fast was each one going, and had it recently moved or slowed down? Was there sufficient room next to the drivers’ car?
They used some of the data to train the algorithm, then put the computer behind the wheel in re-runs of the simulations. The algorithm could predict accurately when the driver would attempt a lane change. Such algorithms would help a self-driving car make smarter decisions in the moment. They could also be used to teach the cars to mimic human driving tics.