Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models

TL;DR

This paper introduces an improved accelerated evaluation method for automated vehicles using Piecewise Mixture Distribution models, significantly reducing testing time while maintaining statistical accuracy.

Contribution

It advances the accelerated evaluation framework by replacing single parametric models with Piecewise Mixture Distributions, enhancing accuracy and efficiency.

Findings

Outperforms single parametric models in accuracy and efficiency

Reduces evaluation time by nearly four orders of magnitude

Successfully models cut-in lane change behavior using real-world data

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.