Comparison of Vehicle Lateral Movement Models for Automated Driving Function Validation

The range of vision of vehicle sensors used by automated driving functions is considerably influenced by the lateral movement of vehicles within their lane. With the increasing relevance of simulations for the validation of automated driving functions, realistic modeling of this lateral movement gains in importance. Different stochastic models addressing this task have been proposed in literature. The used datasets and performed evaluations, however, are diverse, hampering the comparison of the models. Further, in some cases the evaluated features are limited, restricting the assessment of the model's applicability for a wide range of use cases. This work therefore starts with the identification of a suitable uniform data basis and evaluation strategy enabling the latter. Based on this, the models are compared in terms of qualitative and quantitative results and runtime. The results reveal strengths and limitations of the models, indicate suitable use cases, and reveal potentials for improvement.