Interaction Models for Merging and Cut-in Scenarios

Experienced human drivers generally try to con-sider not only the safety of their own vehicle but also to avoid disturbing surrounding vehicles in a way that could negatively affect the flow of traffic or even cause accidents. This requires an estimation of the possible reaction of other traffic participants. This paper addresses this kind of interaction model and proposes qLPV models for two important scenarios, merging and cut-in, which have high importance for safety and traffic fluidity. The proposed models rely only on available datasets, and sparse identification methods are used to identify their parameters. Drone measurements from Germany and China are used for identification and evaluation.