Data-Driven Lane Change Modeling for Automated Driving Function Validation

The lateral movement of vehicles is an important indicator for the prediction of cut-ins in automated driving (AD) functions, and a relevant factor for the effective perception range of AD sensors. With simulations being an integral part of the validation of AD functions, models to realistically reflect the lateral movement of vehicles are crucial in order to generate realistic inputs for the AD system’s sensors and algorithms. Earlier work therefore proposed a two-level stochastic model for the lateral movement of vehicles on highways, which was, however, limited to lane following maneuvers. Within this work, the model is extended towards a full lateral movement model for highway scenarios by extending it towards lane changes. The proposed complete model represents a consistent generalization of the previous lane following model, in sharing model components and parameters, and in maintaining a measurably high degree of realism and efficiency in simulation.