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.

翻译：自动驾驶系统的部署带来了基础性的费率厘定难题：经验数据稀疏、运行设计域不断变化，以及不同软件版本间的非平稳风险。我们提出了一种分层贝叶斯信度框架，通过一种基于学习得到的ODD相似性核，在城市、软件版本及区域之间进行信息池化，并将Buhlmann-Straub方法作为其极限情形。基于美国国家公路交通安全管理局《常设通用命令》数据库中，四座美国都市区Waymo车辆在6.48亿次匹配行驶里程中记录的648起已确认介入事故进行验证，结果显示：城市聚合信度权重处于中等水平（0.12-0.46），部分池化策略显著优于无池化策略，且功效分析表明，当部署城市数量达到约十二座时，学习所得核函数的优势即变得可检测。