Trajectory Planning for Automated Vehicles using Driver Models

Behavioral-specific trajectory planning for automated vehicles is an intensively explored research topic. Many situations in daily traffic, e.g. following a leading vehicle or stopping behind it, require knowledge about how the scene may evolve. In recent years, much effort has been put into developing driver models to predict traffic scenes as realistic as possible according to human behavior. In this paper, we present a method for behavioral-specific trajectory planning using dedicated driver models. The main idea is to first calculate a reference trajectory using a suitable model to achieve the desired behavior and then to incorporate this reference trajectory into an optimal control problem to obtain an acceleration- and jerk-optimal trajectory. A major strength of this method is in the small computation time, since the problem is formalized as a quadratic optimization problem and can thus be efficiently solved in real time, even for a huge number of optimization variables.