Characteristic Stripe Pattern Masks Creation for Driving Maneuvers Identification Using Synthetic Data

Abstract

Previous research has demonstrated that encoding n-dimensional driving data using space-filling curves reveals visual patterns that we call CSPs, which repeat among trajectories of the same type (e.g., roundabout passings, turns, braking, etc.). Unlike traditional methods, which rely on a limited number of real data samples to manually create binary CSP-masks for event identification, our approach systematically creates CSP-masks based on synthetic data. With this, we can explore in a structured way combination of factors such as speed, acceleration, and more that may help to determine the performance of software components, which process multi-dimensional, time-series data for pattern identification, to support testing of software components. To systematically create these masks for event identification, we compile the CSPs for each type of maneuver and cluster occurring stripes (same as clustering the indices).