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.
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Acknowledgements
The authors would like to thank all partners within the Hi-Drive project for their co-operation and valuable contribution. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 101006664. The sole responsibility of this publication lies with the authors. Neither the European Commission nor CINEA—in its capacity of Granting Authority—can be made responsible for any use that may be made of the information this document contains.
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Forneris, L. et al. (2024). A Synthetic Dataset Generator for Automotive Overtaking Maneuver Detection. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_52
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DOI: https://doi.org/10.1007/978-3-031-48121-5_52
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