平面とサンプル：サブモジュラー最適化による自動運転車性能の情報最大化
Plane and Sample: Maximizing Information about Autonomous Vehicle Performance using Submodular Optimization
自動運転車の評価シナリオ選択をサブモジュラー最適化として定式化し、情報利得に基づく停止基準を提案。シナリオ空間の約7.5%の探索で評価を完了できることを示した。
著者: Anne Collin, Amitai Y. Bin-Nun, Radboud Duintjer Tebbens
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
As autonomous vehicles (AVs) take on growing Operational Design Domains (ODDs), they need to go through a systematic, transparent, and scalable evaluation process to demonstrate their benefits to society. Current scenario sampling techniques for AV performance evaluation usually focus on a specific functionality, such as lane changing, and do not accommodate a transfer of information about an AV system from one ODD to the next. In this paper, we reformulate the scenario sampling problem across ODDs and functionalities as a submodular optimization problem. To do so, we abstract AV performance as a Bayesian Hierarchical Model, which we use to infer information gained by revealing performance in new scenarios. We propose the information gain as a measure of scenario relevance and evaluation progress. Furthermore, we leverage the submodularity, or diminishing returns, property of the information gain not only to find a near-optimal scenario set, but also to propose a stopping criterion for an AV performance evaluation campaign. We find that we only need to explore about 7.5% of the scenario space to meet this criterion, a 23% improvement over Latin Hypercube Sampling.
