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Developing a Toolchain for Synthetic Driving Scenario Datasets

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Marianna Cossu
Riccardo Berta
Alessio Capello
Alessandro De Gloria
Luca Lazzaroni
Francesco Bellotti
Published in
Applications in Electronics Pervading Industry, Environment and Society
Publisher
Springer Nature Switzerland

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Abstract

The chapter introduces a toolchain for synthetically generating driving scenario datasets, crucial for verifying on-road automatic driving functions (ADF). It leverages the Car Learning to Act (CarLA) simulator to create various driving scenarios with high variability in environmental and scenario-specific parameters. The toolchain allows users to specify parameters through a JSON configuration file, ensuring ease of use and flexibility. The paper discusses the architecture, user parameters, scenario generation, simulation results, and challenges encountered during development. Early results demonstrate the toolchain's potential in generating diverse and realistic datasets, which can complement real-world data, particularly in addressing corner cases.
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previous chapter Diagnostic Analytics for Pixelated Particle Detectors: A Case Study
next chapter Ticketing Systems for Smart Public Transportation: Tools at the User Side
Title
Developing a Toolchain for Synthetic Driving Scenario Datasets
Authors
Marianna Cossu
Riccardo Berta
Alessio Capello
Alessandro De Gloria
Luca Lazzaroni
Francesco Bellotti
Copyright Year
2023
Book
Applications in Electronics Pervading Industry, Environment and Society

Print ISBN: 978-3-031-30332-6

Electronic ISBN: 978-3-031-30333-3

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-30333-3_29
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