An Accelerated Testing Approach for Automated Vehicles with Background Traffic Described by Joint Distributions
For developers of automated vehicles, this provides a more realistic and efficient testing framework for rare-event risk assessment in naturalistic traffic.
This paper extends accelerated evaluation for automated vehicles from independent to joint statistical models, using importance sampling and monotonicity to handle high-dimensional rare events, achieving reduced test cost.
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. Furthermore, the monotonic property is used to avoid the curse of dimensionality introduced by the joint distributions. Simulation results show that the procedure is effective and has a great potential to reduce the test cost for automated vehicles.