Anticipating others' behavior on the road

Humans may be one of the biggest roadblocks keeping fully autonomous vehicles off city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, cyclists, and pedestrians are going to do next. Behavior prediction is a tough problem, however, and current artificial intelligence solutions are either too simplistic (they may assume pedestrians always walk in a straight line), too conservative (to avoid pedestrians, the robot just leaves the car in park), or can only forecast the next moves of one agent (roads typically carry many users at once.) MIT researchers have devised a deceptively simple solution to this complicated challenge. They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time. Their behavior-prediction framework first guesses the relationships between two road users -- which car, cyclist, or pedestrian has the right of way, and which agent will yield -- and uses those relationships to predict future trajectories for multiple agents.