Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift
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Computer Science > Machine Learning
Title:Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Robotics (cs.RO) |
| Cite as: | arXiv:2606.17451 [cs.LG] |
| (or arXiv:2606.17451v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17451
arXiv-issued DOI via DataCite (pending registration)
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