Title: Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models,
Abstract: The process to certify highly Automated Vehicles has not yet been defined by
any country in the world. Currently, companies test Automated Vehicles on
public roads, which is time-consuming and inefficient. We proposed the
Accelerated Evaluation concept, which uses a modified statistics of the
surrounding vehicles and the Importance Sampling theory to reduce the
evaluation time by several orders of magnitude, while ensuring the evaluation
results are statistically accurate. In this paper, we further improve the
accelerated evaluation concept by using Piecewise Mixture Distribution models,
instead of Single Parametric Distribution models. We developed and applied this
idea to forward collision control system reacting to vehicles making cut-in
lane changes. The behavior of the cut-in vehicles was modeled based on more
than 403,581 lane changes collected by the University of Michigan Safety Pilot
Model Deployment Program. Simulation results confirm that the accuracy and
efficiency of the Piecewise Mixture Distribution method outperformed single
parametric distribution methods in accuracy and efficiency, and accelerated the
evaluation process by almost four orders of magnitude.