Reusing Operational Evidence After Context Changes: A Conservative Bayesian Framework for Autonomous Vehicle Safety

2026-08-04

AI Summary

This work addresses the challenge of effectively reusing historical fault-free operational evidence when the operational design domain (ODD) of an autonomous driving system changes. To this end, the authors propose a safety assessment framework grounded in Conservative Bayesian Inference (CBI). Moving beyond the traditional “all-or-nothing” paradigm of evidence reuse, the approach enables partial, transparent, and conservative transfer of operational evidence between old and new ODDs by constructing a constrained set of prior distributions and incorporating a weighted evidence fusion mechanism. The framework yields conservative posterior estimates of system reliability and supports credible safety claims in the new environment. Numerical experiments demonstrate its effectiveness in efficiently leveraging prior evidence to substantiate safety assurances under domain shifts.

Abstract

Operational evidence, i.e., evidence of operation without failure is an important component of confidence in the safety or reliability of a system in service, but it is costly to collect. When the context of operation changes, the relevance of previously collected operational evidence becomes unclear. This problem arises, for example, when an autonomous vehicle that has operated safely in one operational environment (the Target Operational Domain, TOD) is deployed in a different but related TOD. Existing practice often treats such evidence in an all-or-nothing manner: either it is fully reused, or it is discarded. Neither position is satisfactory when there are good reasons to believe that the new context is no worse than the previous one, but that belief is itself uncertain. This paper studies how to make post-change reliability claims by combining evidence from two TODs using Conservative Bayesian Inference (CBI), which combines the evidence from the new context with a weighted amount of previous context and partial prior knowledge through constraints on a set of admissible priors. This yields conservative posterior bounds on quantities of interest. A numerical example illustrates how pre-existing evidence from a previous TOD can be transferred, conservatively and transparently, to support reliability claims in the changed TOD.

Problem

• operational evidence
• context change
• autonomous vehicle safety
• reliability claim
• Target Operational Domain

Innovation

• Conservative Bayesian Inference
• Operational Evidence Reuse
• Autonomous Vehicle Safety
• Target Operational Domain
• Reliability Assessment