Legible Model Predictive Control for Autonomous Driving on Highways

Safety and efficiency are two defining factors for autonomous vehicles. While there is already extensive literature on how to safely cope with highway situations, approaches for higher efficiency are usually designed on an individual basis and are often accompanied by an increased risk of collision. Here, in addition to focusing on the individual behavior of the autonomous ego vehicle, we also consider how to support other traffic participants in correctly inferring the ego vehicle's future maneuvers, thus, enabling secure and efficient traffic flow. We propose a legible model predictive control method that provides a framework to improve the readability of the ego vehicle's planned maneuvers, while simultaneously optimizing factors such as comfort and energy efficiency. A simulation of a highway scenario is presented to demonstrate the effectiveness of our proposed method.