Fast online parameter estimation of the Intelligent Driver Model for trajectory prediction

In this paper, we propose and analyze a method for trajectory prediction in longitudinal car-following scenarios. Hereby, the prediction is realized by a longitudinal car-following model (Intelligent Driver Model, IDM) with online estimated parameters. Previous work has shown that IDM online parameter adaptation is possible but difficult and slow, while providing only small improvement of prediction quality over e.g. constant velocity or constant acceleration baseline models.In our approach (Online IDM, OIDM), we use the difference between a parameter-specific trajectory and the real past trajectory as objective function of the optimization. Instead of optimizing the model parameters “directly”, we gain them based on a weighted sum of a set of prototype parameters, optimizing these weights.To show the benefits of the method, we compare the properties of our approach against state-of-the-art prediction methods for longitudinal driving, such as Constant Velocity (CV), Constant Acceleration (CA) and particle filter approaches on an open freeway driving dataset. The evaluation shows significant improvements in several aspects: (I) The prediction accuracy is significantly increased, (II) the obtained parameters exhibit a fast convergence and increased temporal stability and (III) the computational effort is reduced so that an online parameter adaptation becomes feasible.