運行設計領域の変化下における自動運転車賠償責任の信頼性重み付け価格設定
Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift
自動運転車の保険料率設定におけるデータ不足と運行環境変化の問題に対し、階層ベイズ信頼性モデルを提案し、実際の事故データで有効性を検証した論文。
著者: Doyeon Jang
分類: cs.LG, cs.RO
原文アブストラクト
Automated Driving System deployments create a foundational ratemaking challenge: sparse experience, shifting operational design domains, and non-stationary risk across software releases. We propose a hierarchical Bayesian credibility framework pooling across cities, software versions, and territories via a learned ODD-similarity kernel, nesting Buhlmann-Straub as a limiting case. Demonstrated on 648 verified-engaged Waymo crashes across four U.S. metros from the NHTSA Standing General Order database against 116 million matched miles, city-aggregate credibility weights are moderate (0.12-0.46), partial pooling decisively outperforms no pooling, and a power analysis shows the learned kernel's advantage becomes detectable at approximately twelve deployed cities.