Abstract:

In this paper， scenario dimension reduction and sampling methods are used to optimize the scenario library. Firstly， scenario elements are classified， with their importance weights solved by the Analytic Hierarchy Process， based on which the scenario elements are discretized to construct the scenario space. Then， the risk degree of scenarios are calculated by the attributes of the scenario space itself and the occurrence probability of scenarios in the scenario space are calculated using the Natural Driving Database to construct the importance function. The critical scenarios are screened out from the artificially constructed scenario space through the guidance of the Natural Driving Database to form the testing scenario database. At the same time， to speed up the search process， the multi-starting optimization algorithm and the flood-filling algorithm are used for sampling search. Finally， the effectiveness of the critical scenarios is verified by the scenario risk assessment method. The results show that the scenario library optimization method proposed in this paper can screen out critical scenarios for automatic driving test， and improve the efficiency and practical significance of automatic driving testing.

Key words: automated vehicles, scenario library optimization, scenario occurrence probability, scenario risk degree, critical scenarios screening