This paper presents a novel tool for generating driving scenario datasets, that are a key asset to advance research and development in automated driving and driver assistance systems. The tool relies on the MATLAB. Automated Driving Toolbox and focuses on the overtaking maneuver. It uses simulated vehicular data, without relying on camera-equipped real-world vehicles, thus providing a low-cost solution, while allowing to abstract the main action features, that are very important for the pre-training of machine learning models. The tool has been designed to target customization (in terms, e.g., of road curvature radii), in order to allow meeting specific requirements, while its interoperability (e.g., multiple-format export) supports integration with other development environments. A preliminary analysis of the first scenarios generated with the tool confirms the validity of the system under development.

A Synthetic Dataset Generator for Automotive Overtaking Maneuver Detection

Forneris L.;Berta R.;Capello A.;Cossu M.;Fresta M.;Bellotti F.
2024-01-01

Abstract

This paper presents a novel tool for generating driving scenario datasets, that are a key asset to advance research and development in automated driving and driver assistance systems. The tool relies on the MATLAB. Automated Driving Toolbox and focuses on the overtaking maneuver. It uses simulated vehicular data, without relying on camera-equipped real-world vehicles, thus providing a low-cost solution, while allowing to abstract the main action features, that are very important for the pre-training of machine learning models. The tool has been designed to target customization (in terms, e.g., of road curvature radii), in order to allow meeting specific requirements, while its interoperability (e.g., multiple-format export) supports integration with other development environments. A preliminary analysis of the first scenarios generated with the tool confirms the validity of the system under development.
2024
978-3-031-48120-8
978-3-031-48121-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1163924
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