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This paper proposes a new framework based on joint statistical models forevaluating risks of automated vehicles in a naturalistic driving environment.The previous studies on the Accelerated Evaluation for automated vehicles areextended from multi-independent-variate models to joint statistics. Theproposed toolkit includes exploration of the rare event (e.g. crash) sets andconstruction of accelerated distributions for Gaussian Mixture models usingImportance Sampling techniques. Furthermore, the monotonic property is used toavoid the curse of dimensionality introduced by the joint distributions.Simulation results show that the procedure is effective and has a greatpotential to reduce the test cost for automated vehicles.

An Accelerated Testing Approach for Automated Vehicles with Background Traffic Described by Joint Distributions