Evaluation of Automated Vehicles in the Frontal Cut-in Scenario - an Enhanced Approach using Piecewise Mixture Models

TL;DR

This paper enhances automated vehicle evaluation by employing Piecewise Mixture Distribution models within an accelerated evaluation framework, significantly improving accuracy and efficiency in lane change scenario testing.

Contribution

It introduces the use of Piecewise Mixture Distribution models in the accelerated evaluation of AVs, outperforming single distribution models in accuracy and efficiency.

Findings

Piecewise Mixture Models improve evaluation accuracy.

The method reduces evaluation time by several orders of magnitude.

Simulation confirms superior performance over single distribution models.

Abstract

Evaluation and testing are critical for the development of Automated Vehicles (AVs). Currently, companies test AVs on public roads, which is very 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 final evaluation results are accurate. In this paper, we further extend this idea by using Piecewise Mixture Distribution models instead of Single Distribution models. We demonstrate this idea to evaluate vehicle safety in lane change scenarios. The behavior of the cut-in vehicles was modeled based on more than 400,000 naturalistic driving 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 are better than the single distribution.