Developing a Synthetic Dataset for Driving Scenarios

Motta, Jacopo; Bellotti, Francesco; Berta, Riccardo; Capello, Alessio; Cossu, Marianna; Gloria, Alessandro De; Lazzaroni, Luca; Bonora, Stefano

doi:10.1007/978-3-030-95498-7_43

Cited by 4 publications

(1 citation statement)

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“…The platform establishes a model library that includes a vehicle dynamics model, a sensor model, and a traffic environment model. Motta et al (23) presented a system for generating synthetic datasets including driving scenarios that were based on OpenSCENARIO, a well-established and vendor-independent standard. The user can specify parameter values, such as vehicle speed, distance, and weather condition, which represent the intended variability within a scenario through the system configuration.…”

Section: Related Workmentioning

confidence: 99%

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

Kim¹,

Jang²

2022

Sensors and Materials

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Deep learning technologies that use road images to recognize autonomous driving environments have been actively developed. Such deep-learning-based autonomous driving technologies need a large amount of training data that can represent various road, traffic, and weather environments. However, there have been many difficulties in terms of time and cost in collecting training data that can represent various road environments. Therefore, in this study, we attempt to build a virtual road environment and develop a system for collecting training data based on the virtual environment. To build a virtual environment identical to the real world, we convert and use two kinds of existing geospatial data: high-definition 3D buildings and highdefinition roads. We also develop a system for collecting training data running in the virtual environment. The implementation results of the proposed system show that it is possible to build a virtual environment identical to the real world and to collect specific training data quickly and at any time from the virtual environment with various user-specified settings.

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Section: Related Workmentioning

confidence: 99%