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Scenario-based testing for the safety validation of highly automated vehiclesis a promising approach that is being examined in research and industry. Thisapproach heavily relies on data from real-world scenarios to derive thenecessary scenario information for testing. Measurement data should becollected at a reasonable effort, contain naturalistic behavior of road usersand include all data relevant for a description of the identified scenarios insufficient quality. However, the current measurement methods fail to meet atleast one of the requirements. Thus, we propose a novel method to measure datafrom an aerial perspective for scenario-based validation fulfilling thementioned requirements. Furthermore, we provide a large-scale naturalisticvehicle trajectory dataset from German highways called highD. We evaluate thedata in terms of quantity, variety and contained scenarios. Our datasetconsists of 16.5 hours of measurements from six locations with 110 000vehicles, a total driven distance of 45 000 km and 5600 recorded complete lanechanges. The highD dataset is available online at: http://www.highD-dataset.com

The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems