Enabling safe freeway driving for automated vehicles

The development of automated vehicles brings new challenges to road safety. The behavior of the automated vehicles should be carefully designed in order to interact with the environment and other vehicles efficiently and safely. This paper is focused on the learning and decision making methods for the automated vehicles towards safe freeway driving. Based on a multi-agent traffic model, the decision making problem is posed as an optimal control problem, which is solved by 1) behavior classification and trajectory prediction of the surrounding vehicles, and 2) a unique parallel planner architecture which addresses the efficiency goal and the safety goal separately. The simulation results demonstrate the effectiveness of the algorithm.