Open Access

Lars Klitzke, Carsten Koch, Frank Köster

• For the introduction of new automated driving functions, the systems need to be verified extensively. A scenario-driven approach has become an accepted method for this task. But to verify the functionality of an automated vehicle in the simulation in a certain scenario such as a lane change, relevant characteristics of scenarios need to be identified. This, however, requires to extract these scenarios from real-world drivings accurately. For that purpose, this work proposes a novel framework based on a set of unsupervised learning methods to identify lane-changes on motorways. To represent various types of lane changes, the maneuver is split up into primitive driving actions with an Hidden Markov Model and Divisive Hierarchical Clustering. Based on this, lane change maneuvers are identified using Dynamic-Time-Warping. The presented framework is evaluated with a real-world test drive and compared to other baseline methods. With a f1 score of 98.01\% in lane-change identification, the presented approach shows promising results.