コネクテッド・自動運転車のテストシナリオライブラリ生成：ケーススタディ
Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies
追い越し・高速出口・車間追従の3ケースでテストシナリオライブラリ生成手法を実装し、強化学習で高次元化に対応、評価精度を保ちつつ評価を大幅高速化した。
著者: Shuo Feng, Yiheng Feng, Haowei Sun, Shao Bao, Yi Zhang, Henry X. Liu
分類: cs.RO, cs.AI
原文アブストラクト
Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general methodology for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensions, the proposed methodology is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed methods can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method.
