Abstract
Numerous methods have been developed for testing Connected and Automated Vehicles (CAV). The scenario-based approach is considered the most promising as it reduces the number of scenarios required to certify the CAV system. In this study, we propose a refined six-step methodology that includes two additional steps to compute a critical index for scenarios and use it to guide the sampling process The methodology starts with the generation of functional scenarios using a 5-layer ontology. Next, the driving data is processed to determine the criticality indices of the functional scenarios. This is achieved by using a latent Dirichlet Allocation technique and a Least Means Squares method. Finally, the sampling process is built on a scenario reduction based on clustering and a specific metric related to the a priori criticality indices. Overall, our refined approach enhances the scenario-based methodology by incorporating criticality indices to guide the sampling process, which can reduce drastically the number of scenarios needed for certification of CAV systems.
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- 1.
Pegasus Project and Open Scenario (ASAM).
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Blache, H., Laharotte, PA., El Faouzi, NE. (2023). Towards an Effective Generation of Functional Scenarios for AVs to Guide Sampling. In: Guiochet, J., Tonetta, S., Schoitsch, E., Roy, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops. SAFECOMP 2023. Lecture Notes in Computer Science, vol 14182. Springer, Cham. https://doi.org/10.1007/978-3-031-40953-0_22
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