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

Understand

This paper proposes a new framework based on joint statistical models for evaluating risks of automated vehicles in a naturalistic driving environment.

• The previous studies on the Accelerated Evaluation for automated vehicles are extended from multi-independent-variate models to joint statistics.
• The proposed toolkit includes exploration of the rare event (e.g.
• crash) sets and construction of accelerated distributions for Gaussian Mixture models using Importance Sampling techniques.