Communicating intent on the road through human-inspired control schemes

Given the current capabilities of autonomous vehicles, one can easily imagine autonomy released on the road in the near future. However, it can be assumed that the transition will not be instantaneous, meaning they will have to be capable of driving well in a mixed environment, with both humans and other autonomous vehicles on the road. This leaves a number of concerns for autonomous vehicles in terms of dealing with human uncertainty and understanding of cooperation on the road. This work demonstrates the need for focusing on communication and collaboration between autonomy and human drivers. After analyzing how drivers perform cooperative maneuvers (e.g. lane changing), key cues were identified for conveying intent through nonverbal communication. It was found that human observers can predict lane changes with over two seconds in prior to the lane departure, without use of a turning signal. Building on this concept, an autonomous control scheme is proposed that aims to capture these subtle motions before executing a lane change. To compare the proposed human-inspired methods, three possible control schemes for autonomous vehicles are implemented for a validation study on human subjects to provide feedback on their experience. By properly conveying intent through nuanced trajectory planning, we show that drivers can predict the autonomous vehicle's actions with 40% increase in prediction time when compared to traditional control methods, both as a passenger and while observing the autonomous vehicle.