区分混合モデルによる自動運転車の加速評価
Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models
車線変更車両の挙動を区分混合分布でモデル化し、重要度サンプリングと組み合わせることで自動運転車の評価を約1万倍高速化した研究。
著者: Zhiyuan Huang, Ding Zhao, Henry Lam, David J. LeBlanc
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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.
