| Both sides previous revisionPrevious revision | |
| en:safeav:softsys:vaicomp [2026/04/24 09:42] – raivo.sell | en:safeav:softsys:vaicomp [2026/04/29 16:12] (current) – raivo.sell |
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| A. AI COMPONENT VALIDATION | ===== AI COMPONENT VALIDATION ===== |
| Both the automotive and airborne spaces have reacted to AI by viewing it as “specialized Software” in standards such as ISO 8800 [14] and [13]. This approach has the great utility of leveraging all the past work in generic mechanically safety and past work in software validation. However, now, one must manage the issue of how to handle the fact that we have a data generated “code” vs conventional programming code. In the world of V&V, this difference is manifested in three significant aspects: coverage analysis, code reviews, and version control. | Both the automotive and airborne spaces have reacted to AI by viewing it as “specialized Software” in standards such as ISO 8800 [14] and [13]. This approach has the great utility of leveraging all the past work in generic mechanically safety and past work in software validation. However, now, one must manage the issue of how to handle the fact that we have a data generated “code” vs conventional programming code. In the world of V&V, this difference is manifested in three significant aspects: coverage analysis, code reviews, and version control. |
| TABLE III | |
| V&V Technique Software AI/ML | ^ V&V Technique ^ Software ^ AI/ML ^ |
| Coverage Analysis: Code Structure provides basis of coverage No structure | | Coverage Analysis | Code Structure provides basis of coverage | No structure | |
| Code Reviews: Crowd source expert knowledge No Code to Review | | Code Reviews | Crowd source expert knowledge | No Code to Review | |
| Version Control Careful construction/release Very Difficult with data | | Version Control | Careful construction/release | Very Difficult with data | |
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| These differences generate an enormous issue for intelligent test generation and any argument for completeness. This is an area of active research, and two threads have emerged: | These differences generate an enormous issue for intelligent test generation and any argument for completeness. This is an area of active research, and two threads have emerged: |
| 2) Robustness to Noise: Either through simulation or using formal methods [17], the approach is to assert various higher-level properties and use these to test the component. An example in object recognition might be to assert the property that an object should be recognized independent of orientation. | 2) Robustness to Noise: Either through simulation or using formal methods [17], the approach is to assert various higher-level properties and use these to test the component. An example in object recognition might be to assert the property that an object should be recognized independent of orientation. |
| Overall, developing robust methods for AI component validation is quite an active and unsolved research topic for “fixed” function AI components. That is, AI components where the function is changing with active version control. Of course, many AI applications prefer a model where the AI component is constantly morphing. Validating the morphing situation is a topic of future research. | Overall, developing robust methods for AI component validation is quite an active and unsolved research topic for “fixed” function AI components. That is, AI components where the function is changing with active version control. Of course, many AI applications prefer a model where the AI component is constantly morphing. Validating the morphing situation is a topic of future research. |
| B. AI SPECIFICATION | |
| | ===== AI SPECIFICATION ===== |
| For well-defined systems with an availability of system level abstractions, AI/ML components significantly increase the difficulty of intelligent test generation. With a golden spec, one can follow a structured process to make significant progress in validation and even gate the AI results with conventional safeguards. Unfortunately, one of the most compelling uses of AI is to employ it in situations where the specification of the system is not well defined or not viable using conventional programming. In these Specification Less /ML (SLML) situations, not only is building interesting tests difficult, but evaluating the correctness of the results creates further difficulty. Further, most of the major systems (perception, location services, path planning, etc.) in autonomous vehicles fall into this category of system function and AI usage. To date, there have been two approaches to attack the lack of specification problem: Anti-Spec and AI-Driver. | For well-defined systems with an availability of system level abstractions, AI/ML components significantly increase the difficulty of intelligent test generation. With a golden spec, one can follow a structured process to make significant progress in validation and even gate the AI results with conventional safeguards. Unfortunately, one of the most compelling uses of AI is to employ it in situations where the specification of the system is not well defined or not viable using conventional programming. In these Specification Less /ML (SLML) situations, not only is building interesting tests difficult, but evaluating the correctness of the results creates further difficulty. Further, most of the major systems (perception, location services, path planning, etc.) in autonomous vehicles fall into this category of system function and AI usage. To date, there have been two approaches to attack the lack of specification problem: Anti-Spec and AI-Driver. |
| 1) Anti-Spec | 1) Anti-Spec |
