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 characteristic of scenarios need to be identified. That, 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 a Hidden Markov Model (HMM) and Divisive Hierarchical Clustering (DHC). Based on this, lane-change maneuvers are identified using Dynamic Time Warping (DTW). 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.


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    Title :

    Identification of Lane-Change Maneuvers in Real-World Drivings With Hidden Markov Model and Dynamic Time Warping


    Contributors:


    Publication date :

    2020-09-20


    Size :

    1481802 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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