AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation

Klaus Dietmayer; Knut Graichen; Lars Ullrich; Michael Buchholz

AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation

T0 review · 0 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read AI safety assurance for automated vehicles must become data-driven and lifecycle-wide, not rule-based and design-time.

desk verdict A solid, useful survey of AV AI safety assurance across research, standards, and regulation; the 'data-driven assurance is necessary' thesis is a well-argued agenda but overstates its logical status. read the letter →

arxiv 2504.18328 v1 pith:JHEQYVMS submitted 2025-04-25 cs.CY

Lars Ullrich , Michael Buchholz , Klaus Dietmayer , Knut Graichen This is my paper · ORCID

classification cs.CY

keywords AIsafetyassuranceautomatedvehiclesdata-drivenfunctionalSOTIFlifecyclestandardizationregulation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that safety assurance for AI-based automated vehicles cannot remain a design-time, rule-based exercise. Because modern AI systems learn implicit, data-dependent relationships, the authors maintain that a general, formal, closed-form safety proof is contradictory, so assurance must become data-driven and lifecycle-wide. The survey reaches this conclusion by jointly reviewing safety research, standardization, and regulation, and it identifies data dependency, lifecycle consideration, and safety abstraction as the three core features any future assurance approach must address. If the argument is right, future safety cases will center on data quality, data lifecycle, and operational monitoring rather than only on inherited functional-safety artifacts.

What carries the argument

The central mechanism is the idea of data-driven AI safety assurance, defined as an assurance approach that builds on implicit assumptions embedded in data and on data-based verification and validation of those assumptions, extending to data-based system analysis over the lifecycle. Its named counterparts are functional safety and SOTIF, which the paper proposes to extend into data-based functional safety and data-based SOTIF, together with the emerging AI-SIL classification. This mechanism does the work of turning the premise that AI systems are implicit and data-dependent into a constructive alternative: assurance becomes a repetitive cycle of exploration, observation, and mitigation, supported by out-of-distribution detection, simulation-based validation, and periodic offline updates.

What would settle it

A concrete demonstration that a production-scale neural network used in automated driving, such as a perception or trajectory-prediction model, can be formally verified to satisfy safety properties across its full operational data distribution would falsify the premise that a general closed-form solution is contradictory; conversely, showing that such verification remains intractable or incomplete would support the paper's case.

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Extended reading notes

Core claim

The central discovery is the thesis that safety assurance for automated vehicles must shift from rule-based, design-time methods to data-based assurance across the entire AI lifecycle. On the paper's own terms, familiar assurance methods such as functional safety and SOTIF should be extended into data-based functional safety and data-based SOTIF, with data itself pivotal. The authors ground this in the observation that AI behavior is determined both by training data and by operational data, and that fine-tuning or updates invalidate earlier proofs, so verification and validation must become a repetitive cycle of exploration, observation, and mitigation. They also contend that the current gap in automotive standardization and the heterogeneity of global regulation make this shift necessary for any practical deployment, and they propose non-legally-binding open standards and closer networking of research, standardization, and regulation as the way forward.

Load-bearing premise

The argument hinges on the premise that a general, formal, closed-form safety proof for AI systems is contradictory because these systems implicitly map highly complex, non-trivial, data-dependent relationships; if scalable formal verification for neural networks matures, the necessity of the data-driven shift is weakened.

Editorial extensions

If this is right

Future safety cases for automated vehicles will center on data quality, data lifecycle, and operational monitoring, not only on design-level functional safety.
Functional safety and SOTIF standards would need to be extended into data-based functional safety and data-based SOTIF, following the direction already sketched by the AI-SIL concept.
Regulatory approval would become iterative: periodic offline updates and continuous monitoring would replace one-time certification, analogous to regular vehicle technical inspections.
Standardization bodies would need to accelerate automotive-specific AI standards that address data and lifecycle, since general AI standards are ahead of automotive ones.
A technology-agnostic, data-centered methodology would let safety methods transfer across AI architectures and hardware, accommodating future innovations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If data-driven assurance becomes the norm, a safety case becomes a living artifact that must be re-established after every retraining or over-the-air update, turning certification into a continuous process rather than a one-time event.
Operational data collection would become a regulatory requirement, which will likely conflict with privacy rules and data-sharing incentives; a useful test is whether fleet-wide data pooling can support assurance without introducing new biases or liability.
The paper's conclusion depends on the infeasibility of formal closed-form assurance; if scalable formal verification for neural networks matures, the necessity of the data-driven shift weakens, though data-driven monitoring may still be needed as a complement.
A concrete extension would be a side-by-side safety case for one perception function, one built on data-driven out-of-distribution monitoring and one on formal verification of a simplified model, tested against the same distribution shift to see which assurance style degrades more gracefully.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a competent and genuinely useful survey, and the reader's ACCEPT verdict is reasonable. The three-pillar structure — research, standardization, regulation — fills a real gap; most surveys stop at one. The standards tables (I–III) are thorough and current, with work-in-progress standards included, and the regulation overview (EU AI Act timeline, US state patchwork, China) is accurate and well sourced. If you need a compact map of this landscape, this is a good place to start.

What the paper does well: it reads the interdependencies between the three pillars rather than listing them separately. The argument that data dependency undermines classical functional-safety assumptions is made clearly, and the call for lifecycle-oriented, data-based assurance is well grounded in cited literature. The paper is also honest about its own open questions — Section VI explicitly asks how data-based analysis can yield reliable safety statements and admits no general breakthrough exists.

The soft spot is the one the stress-test flags. The necessity claim in Section VII ('transition from rule-based to data-based assurance is necessary') rests on the Section III.A premise that a general, formal, closed-form safety argument for AI 'appears contradictory.' That is a conjecture, not a demonstrated impossibility. Neural-network verification has matured — SMT and abstract-interpretation tools, including those in ISO/IEC 24029-2:2023, which the paper cites, can certify robustness for nontrivial networks, and there are verified controllers (ACAS Xu) in aerospace. So 'necessary' overstates the case; 'currently the most practical path given today's verification limits' would be more accurate. The paper's own admission that data-based assurance is not yet a demonstrated replacement cuts both ways — it makes the proposal honest, but it weakens the 'mandatory' framing. For a survey/position paper, this is a fixable calibration issue, not a fatal flaw.

Minor issues: a duplicated passage in Section V-A (the Parliament amendments paragraph repeats itself), and a few typos ('Morover', 'troughout'). Nothing that affects the substance.

Who it's for: graduate students, safety engineers, and researchers wanting a quick orientation in AV AI safety standards and regulation. It won't change your research direction unless you work in this area, but it's a solid reference. I'd bring it to reading group and would cite it for the standards overview.

Recommendation: yes, it deserves peer review — and it already went through it at IEEE TIV. If this were a fresh submission, I'd send it out and ask the authors to soften the necessity claim to a conditional agenda and merge the duplicated paragraph.

Referee Report

0 major / 7 minor

Summary. This survey jointly reviews research, standardization, and regulation for AI safety assurance in automated vehicles. It argues that current assurance methods are inadequate for AI-based systems and recommends a shift toward data-driven safety assurance over the full lifecycle, with data as the central element. The paper is organized around three pillars: AI safety research (Section III), AI safety standardization (Section IV, including ISO 26262, ISO 21448, ISO/IEC TR 5469, ISO/IEC 24029-2, and the work-in-progress landscape in Table III), and AI regulation (Section V, covering the EU AI Act, US federal and state actions, China, and several other countries). Section VI lists open questions, and Section VII states the paper's perspective that rule-based assurance must transition to data-based assurance.

Significance. The paper's strength is its holistic, well-referenced synthesis: it connects research, standardization, and regulation in a way that earlier surveys do not, and its account of the EU AI Act timeline and the standards landscape is accurate. The authors are explicit that the central recommendation is a perspective ('in our perspective') and they acknowledge in Section VI that a general method for data-based safety analysis is not yet available. The survey thus offers a useful orientation and research agenda rather than a formal proof, which is appropriate for its genre. The paper does not provide machine-checked proofs or quantitative predictions, and it does not need to; its value lies in the structured map of the field and the clearly stated thesis.

minor comments (7)

[Section III.A / VII] The words 'mandatory' in Section III.A and 'necessary' in Section VII are stronger than the immediately hedged premise ('appears contradictory') and than the survey's own acknowledgment in Section VI that no general breakthrough in data-based safety analysis is yet apparent; please temper these terms to 'currently necessary given the state of the art' or 'necessary in the authors' assessment' so that the claim is not read as ruling out scalable formal verification in principle.
[Section VI] Section VI explicitly leaves open how a data-based analysis of AI systems can yield reliable safety statements and states that no general breakthrough is apparent; the conclusion should explicitly connect this to the 'necessary' phrasing by describing data-based assurance as a necessary research direction rather than an established method.
[Section IV.C] The paragraph beginning 'Overall, as it can be seen from Table III...' is duplicated almost verbatim by the following paragraph beginning 'Overall, as shown in Table III...'; one of the two should be deleted.
[Section V.A] In the discussion of the European Parliament's June 2023 position, the sentence beginning 'The most important adjustments include...' is repeated verbatim after 'Beyond that, another crucial adjustment is...'; please remove the duplicate.
[Section V.B] There are several typos: 'troughout' should be 'throughout', 'emphasiszed' should be 'emphasized', 'Publicil' (reference [177]) should be 'Public', 'Morover' in Section IV.B should be 'Moreover', and 'and the and the final part' in Section V.A should read 'and the final part'.
[Section IV.C] The phrase 'in accordance with the title, twice' is unclear; please rephrase to state explicitly which standards have titles that address data.
[Table II] In Table II, 'A VP' in the title of ISO/TS 23374-2 should be 'AVP'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey derives no quantities, makes no fitted predictions, and its central recommendation rests on external standards, regulations, and cited research rather than on self-citation or definitional equivalence.

full rationale

This is a survey paper; it contains no derived equations, no fitted parameters, and no quantitative predictions that could be equivalent to inputs by construction. The central claim that a shift toward data-based safety assurance is 'necessary' is supported by an argument in Section III.A about data dependency of AI systems and the invalidation of prior proofs after fine-tuning, and by references to external literature (e.g., [10], [30]-[32], [35], [36]). The load-bearing premise that 'the notion of a general, formal, closed-form solution appears contradictory' is an explicit conjecture, not a definitional tautology; the paper even acknowledges that 'a formal safety assurance approach that accounts for data dependency might be theoretically possible,' so the conclusion is not embedded in the premise by definition. The only self-citation, [112] by co-author M. Buchholz, appears in Section IV.B as a peripheral pointer for out-of-scope extended-vehicle and V2X topics and does not support any central claim. The paper also candidly flags its own open question in Section VI: 'how a data-based analysis of AI systems can be conducted so that reliable statements about safety can be made,' which is a limitation and an evidentiary weakness but not circularity. The skeptic's concern that formal verification methods may mature is a correctness or overclaim objection, not a circularity objection. Accordingly, the honest finding is no significant circularity, score 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new mathematical quantities, fitted parameters, or entities. Its central claim rests on three domain assumptions about AI opacity, infeasibility of formal methods, and inevitability of residual risk, all drawn from the broader literature.

assumptions (3)

domain assumption AI systems are data-dependent and opaque, so white-box analytical safety methods cannot be applied directly.
Introduced in Section I and used throughout to justify a data-driven assurance paradigm.
domain assumption A general formal closed-form safety solution for AI is contradictory or infeasible.
Section III.A states this to argue that data-driven evaluation is the only realistic path.
domain assumption Residual risk in automated driving cannot be zero, and socially acceptable risk must be defined through regulation and societal discourse.
Section V.E uses this to argue for flexible, non-binding regulatory guidelines.

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Cite this review

Pith. "Pith review of AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation." pith.science (2026). https://pith.science/paper/JHEQYVMS

@misc{pith2026250418328,
  author       = {Pith},
  title        = {Pith review of: AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JHEQYVMS}},
  note         = {Machine review of arXiv:2504.18328}
}

read the original abstract

Assuring safety of artificial intelligence (AI) applied to safety-critical systems is of paramount importance. Especially since research in the field of automated driving shows that AI is able to outperform classical approaches, to handle higher complexities, and to reach new levels of autonomy. At the same time, the safety assurance required for the use of AI in such safety-critical systems is still not in place. Due to the dynamic and far-reaching nature of the technology, research on safeguarding AI is being conducted in parallel to AI standardization and regulation. The parallel progress necessitates simultaneous consideration in order to carry out targeted research and development of AI systems in the context of automated driving. Therefore, in contrast to existing surveys that focus primarily on research aspects, this paper considers research, standardization and regulation in a concise way. Accordingly, the survey takes into account the interdependencies arising from the triplet of research, standardization and regulation in a forward-looking perspective and anticipates and discusses open questions and possible future directions. In this way, the survey ultimately serves to provide researchers and safety experts with a compact, holistic perspective that discusses the current status, emerging trends, and possible future developments.

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Road Vehicles — Application of Predictive Maintenance to Hardware with ISO 26262-5 , Std. ISO/TR 9839:2023, 2023

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Road Vehicles — Test Scenarios for Automated Driving Systems — Vocabulary, Std. ISO 34 501:2022, 2022

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Road Vehicles — Test Scenarios for Automated Driving Systems — Scenario Based Safety Evaluation Framework , Std. ISO 34 502:2022, 2022

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ISO 34 503:2023, 2023

Road Vehicles — Test Scenarios for Automated Driving Systems — Specification for Operational Design Domain , Std. ISO 34 503:2023, 2023

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ISO 34 504:2024, 2024

Road Vehicles — Test Scenarios for Automated Driving Systems — Scenario Categorization, Std. ISO 34 504:2024, 2024

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Intelligent Transport Systems — Geographic Data Files (GDF) GDF5.1, Part 2: Map Data used in Automated Driving Systems, Cooperative ITS, and Multi-Modal Transport , Std. ISO 20 524-2:2020, 2020

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Intelligent Transport Systems — Dynamic Data and Map Database Specification for Connected and Automated Driving System Applica- tions, Part 1: Architecture and Logical Data Model for Harmonization of Static Map Data , Std. ISO 22 726:2023, 2023

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Intelligent Transport Systems — Low-Speed Automated Driving System (LSADS) Service, Part 2: Gap Analysis , Std. ISO/TR 5255-2:2023, 2023

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ISO 20 900:2023, 2023

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