A model identification scheme for driver-following dynamics in road traffic

The driver-following, or car-following, model is one of the most fundamental driver behavior models that are applied in intelligent transport applications. Its fidelity determines the applicability of microscopic traffic simulators, where the model is often implemented to mimic real traffic. Meanwhile, the behavioral model is fundamental to the development of advanced driving assistance systems (ADAS). This paper develops a dynamic model identification approach based on iterative usage of the extended Kalman Filtering (EKF) algorithm. Among other things, this allows to carry out model identification using a rather general optimization objective on the whole physical states of the following vehicle. In particular, the method is established on the basis of the equivalence between the Kalman filter and the recursive least squares (RLS) method in a specific context of parameter identification. To illustrate the method, two car-following models are studied in numerical experiments using real car-following data. The method has shown advantages in replication and prediction of vehicle dynamics in car-following over the conventional approaches. It has also the potential to be further extended for building tactical driving controllers in intelligent transportation applications.