Efficiently Modeling Lateral Vehicle Movement Including its Temporal Interrelations Using a Two-Level Stochastic Model

The lateral movement of vehicles within their lane under homogeneous traffic conditions is decisive for the range of vision of vehicle sensors. It significantly contributes to the maximum situational awareness an automated driving function can achieve. Given the integral role that simulations play in the validation of automated driving functions, the development of models that accurately describe the lateral movement of vehicles within their lane becomes essential. A few models have already been proposed in literature that address this task. Existing models, however, exhibit limitations when it comes to their usage for the virtual validation of automated driving functions such as the replication of general instead of driver-specific behavior and complex calibration methods. Furthermore, the metrics used for evaluation focus on measuring the accordance of the overall lateral offset and speed distribution and do not take into account the temporal course of the lateral offset profiles. Within this work, we introduce a two-level stochastic model addressing the identified limitations. We further present a strategy suitable for evaluating the low-level characteristics of the generated lateral offset profiles relevant for validating an automated driving function such as a cut-in detection function within simulations. The model’s capabilities are demonstrated based on five single driver datasets. It is shown that the model allows for efficient calibration, is able to replicate the behavior of these drivers, and is characterized by short runtimes. This makes it suitable for the virtual validation of automated driving functions.