自動運転の試験場をどう評価するか？定量的アプローチ
How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach
自動運転車の試験場が実世界の交通シナリオをどれだけ再現できているかを、自然走行データから抽出した代表シナリオを用いて定量的に評価する手法を提案し、Mcity・Almono・Kcityの3施設で比較した。
著者: Rui Chen, Mansur Arief, Weiyang Zhang, Ding Zhao
分類: cs.RO
原文アブストラクト
Proving ground has been a critical component in testing and validation for Connected and Automated Vehicles (CAV). Although quite a few world-class testing facilities have been under construction over the years, the evaluation of proving grounds themselves as testing approaches has rarely been studied. In this paper, we present the first attempt to systematically evaluate CAV proving grounds and contribute to a generative sample-based approach to assessing the representation of traffic scenarios in proving grounds. Leveraging typical use cases extracted from naturalistic driving events, we establish a strong link between proving ground testing results of CAVs and their anticipated public street performance. We present benchmark results of our approach on three world-class CAV testing facilities: Mcity, Almono (Uber ATG), and Kcity. We successfully show the overall evaluation of these proving grounds in terms of their capability to accommodate real-world traffic scenarios. We believe that when the effectiveness of a testing ground itself is validated, the testing results would grant more confidence for CAV public deployment.
