Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift
arXiv:2606.17451v1 Announce Type: new 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-aggrega
The increasing deployment of Automated Driving Systems (ADS) by companies like Waymo is creating an urgent need for robust liability and risk assessment frameworks, particularly as operational design domains (ODDs) evolve.
This research directly addresses the foundational challenge of ratemaking for autonomous vehicles, which is critical for their insurance, regulatory approval, and scalable commercial deployment.
The proposed hierarchical Bayesian credibility framework offers a method to price autonomous vehicle liability despite sparse data and shifting operational conditions, potentially enabling more accurate and flexible insurance models.
- · Autonomous Vehicle Developers
- · Insurance Industry (innovators)
- · Regulatory Bodies
- · Consumers of AV technology
- · Traditional Insurance Underwriters
- · Companies with less developed risk assessment models
More accurate and dynamic insurance pricing for autonomous vehicles becomes feasible, reducing a significant barrier to their widespread adoption.
The ability to better quantify and manage risk could accelerate the development and deployment of more advanced and diverse autonomous driving features.
Standardized risk assessment methodologies might emerge, fostering greater regulatory convergence and potentially influencing global AV market development.
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Read at arXiv cs.LG