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A Synthetic Dataset Generator for Automotive Overtaking Maneuver Detection

Authors: Luca Forneris; Riccardo Berta; Alessio Capello; Marianna Cossu; Matteo Fresta; Fabio Tango; Francesco Bellotti;

A Synthetic Dataset Generator for Automotive Overtaking Maneuver Detection

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.

Country
Italy
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Keywords

vehicular data, driving scenario detection, automated driving, driving scenario, Automated driving; Driving scenario; Driving scenario classification; Driving scenario detection; Synthetic datasets; Vehicular data, synthetic datasets, driving scenario classification

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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