Driving test scenarios from real-world driving data is considered as an effective solution for ICV on-road test. The main problem is how to select valid test scenarios from a large number of scenario snapshots. In this paper, a novel test scenario selecting method is proposed. Both the collision risk factor and the traffic factor are considered, and criticality evaluation factors for three typical applications - FCR, LCR and ICR, are defined. A LSTM-AE-Attention model is designed to identify critical scenarios. Experimental results show that the LSTM-AE-Attention based method has rapid convergence and acceptable accuracy while providing critical scenarios is reasonable.
The Critical Scenario Extraction and Identification Method for ICV Testing
24.09.2023
2593383 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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