Abstract
Formalization of driving scenarios is key to define the operational design domain (ODD) of Automated Driving Functions (ADF). Training machine learning (ML) requires huge datasets, that are costly to produce. We propose a toolchain to generate driving scenario video-clip datasets based on the state-of-the-art CarLA driving simulator engine. Scenarios are randomically generated based on a set of parametric features, that are specified by the user. The variability includes both environmental and scenario-specific aspects. As an initial experiment, we have generated a dataset with 200 samples for each one of the 6 implemented classes. The tool is able to achieve a generation rate of about 130 scenarios (7 s. long each) per hour. The tool includes a verification module, which checks the successful completion of each sample.
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References
Weber, H., et al.: A framework for definition of logical scenarios for safety assurance of automated driving. Traffic Inj. Prev. 20, S65–S70 (2019). https://doi.org/10.1080/15389588.2019.1630827
ASAM OpenSCENARIO. https://www.asam.net/standards/detail/openscenario/. Accessed 21 July 2022
Elallid, B.B., Benamar, N., Hafid, A.S., Rachidi, T., Mrani, N.: A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving. J. King Saud Univ. – Comput. Inf. Sci. (2022). https://doi.org/10.1016/j.jksuci.2022.03.013
Korbmacher, R., Tordeux, A.: Review of pedestrian trajectory prediction methods: comparing deep learning and knowledge-based approaches (2021). http://arxiv.org/abs/2111.06740, https://doi.org/10.48550/arXiv.2111.06740
Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A.A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: a survey (2021). http://arxiv.org/abs/2002.00444
Izquierdo, R., Quintanar, A., Parra, I., Fernández-Llorca, D., Sotelo, M.A.: The PREVENTION dataset: a novel benchmark for PREdiction of VEhicles iNTentIONs. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 3114–3121 (2019). https://doi.org/10.1109/ITSC.2019.8917433
Bellotti, F., et al.: Managing big data for addressing research questions in a collaborative project on automated driving impact assessment. Sensors. 20, 6773 (2020). https://doi.org/10.3390/s20236773
Cirimele, V., et al.: The fabric ICT platform for managing wireless dynamic charging road lanes. IEEE Trans. Veh. Technol. 69, 2501–2512 (2020). https://doi.org/10.1109/TVT.2020.2968211
Team, C.: CARLA. http://CarLA.org/. Accessed 21 July 2022
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator (2017). http://arxiv.org/abs/1711.03938, https://doi.org/10.48550/arXiv.1711.03938
CARLA: Car Learning to Act — An Inside Out – ScienceDirect. https://www.sciencedirect.com/science/article/pii/S1877050921025552. Accessed 21 July 2022
Motta, J., et al.: Developing a synthetic dataset for driving scenarios. In: Saponara, S., De Gloria, A. (eds.) ApplePies 2021. LNEE, vol. 866, pp. 310–316. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-95498-7_43
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Cossu, M., Berta, R., Capello, A., De Gloria, A., Lazzaroni, L., Bellotti, F. (2023). Developing a Toolchain for Synthetic Driving Scenario Datasets. In: Berta, R., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2022. Lecture Notes in Electrical Engineering, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-031-30333-3_29
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DOI: https://doi.org/10.1007/978-3-031-30333-3_29
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