Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

The paper proposes a formal approach for describing and evaluating the datasets that are used in automotive applications for machine learning, testing, and validation purposes. Proper, that is, qualitative and quantitative characterization of the datasets can simplify the analysis, evaluation, and comparison of perception-based algorithms designed for highly automated vehicles. Such formalism is also needed to achieve compliance with the automotive industry safety standards that have been recently introduced. Characterization in the form of size or type of raw data, number of recognized and classified objects, and environmental parameters is not perfectly suitable for describing both the static and dynamic aspects of automotive datasets; therefore, another approach is required. In this paper, an efficient method based on an object tracking mechanism, grid representation of the sensor field of view, heatmap concept, and Wasserstein metric is proposed. The efficiency of the method is demonstrated by its ability to handle both the size, properties, and diversity of the dataset, including static and time-varying aspects. The presented description can also be used to compare different datasets and to define the amount of data to be collected.

Abstract licence: CC BY-NC-ND

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