Pith. sign in

REVIEW 3 major objections 5 minor 39 references

FTA generation using GenAI with an Autonomy sensor Usecase

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper shows that a general-purpose LLM, guided only by prompt engineering and error feedback, can generate a structurally valid fault tree for a Lidar sensor failure, rendered with the PlantUML tool.

desk verdict A fairly honest feasibility demo of prompt-engineered PlantUML fault trees, undermined by an abstract that claims success without validating FTA correctness. read the letter →

arxiv 2411.15007 v1 pith:62DYKSGA submitted 2024-11-22 eess.SY cs.AIcs.CRcs.LGcs.SY

Sneha Sudhir Shetiya , Divya Garikapati , Veeraja Sohoni This is my paper · ORCID
classification eess.SYcs.AIcs.CRcs.LGcs.SY
keywords faulttreeanalysisgenerativeAIlargelanguagemodelspromptengineeringPlantUMLLidarsensorautonomousdrivingfunctionalsafety
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

The paper tries to establish that a general-purpose Large Language Model, guided only by prompt engineering and iterative error correction, can produce a fault tree analysis for an autonomous-vehicle sensor failure without dedicated FTA software. The target is the malfunction of a Lidar sensor, and the output is a diagram rendered in PlantUML, a text-based UML diagram tool. If true, this would let safety engineers draft initial fault trees quickly and integrate them into development workflows, reducing manual analysis. The paper frames this as a demonstration of possibility on one use case, with future work needed for other sensors and for FMEA.

What carries the argument

The mechanism is a prompt-engineering loop with PlantUML as the structured output format. PlantUML is a text-based diagram language that renders boxes, circles, and connections into a flowchart; the paper leverages its gate-like symbols to represent OR and AND logic. The user iteratively prompts the LLM to generate FTA content, asks it to express that content as PlantUML code, then feeds any PlantUML syntax errors back into the prompt until the diagram renders cleanly. The standardized FTA symbols (basic events, OR/AND gates, transfer symbols) act as the target representation that the model is trained to produce purely through instruction.

What would settle it

Run the exact final prompt sequence from Table I on a fresh session of the same LLM and ask it to generate a Lidar FTA without prior error feedback; if the output does not render in PlantUML or omits a documented Lidar failure cause such as photodiode damage or adverse weather, then the claim that prompt engineering alone produces valid FTAs for any autonomy use case is falsified.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that an existing open-source LLM, after being asked to generate an FTA for a Lidar sensor and then to convert that into PlantUML code, can be trained through prompts alone to output a structurally valid fault tree. The final tree has a top event "LIDAR Sensor Failure", OR and AND gates drawn as standard symbols, and causes grouped into Hardware, Software, and Environmental categories. When the code initially failed to render, feeding the syntax errors back to the model as prompts led it to correct its own output. The same prompting also handled an extension adding performance-degradation events such as range reduction and slower scan rate.

Load-bearing premise

The paper assumes that a PlantUML diagram that renders without syntax errors, and whose events are grouped into hardware, software, and environmental categories, is a valid fault tree; it never checks whether the fault events and gate logic are correct or complete against a reference FTA.

Editorial extensions

If this is right

  • Fault tree drafting for automotive sensors could become an interactive human-in-the-loop task: an engineer prompts an LLM, reviews the rendered PlantUML diagram, and corrects the prompt rather than building the tree by hand.
  • The same prompt-to-PlantUML pipeline may extend to other autonomous-vehicle sensors such as cameras and radars, at least for producing an initial structural tree.
  • Because PlantUML output is plain text, generated fault trees can be versioned, diffed, and reviewed in standard code-review workflows, making safety artifacts more auditable.
  • With enough prompt refinement, a generalist LLM could serve as an FTA co-pilot that proposes failure-mode decompositions, leaving the final gate logic validation to a safety engineer.
  • The paper's stated next step, using the same approach for FMEA analysis, suggests the prompting pattern could generalize to other safety-analysis artifacts beyond fault trees.

Reading between the lines

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

  • Editorial extension: the evidence demonstrates structural validity, not logical correctness; a real deployment would need to verify that the generated events and gates match a reference FTA for the Lidar failure mode.
  • Editorial extension: the abstract's claim of "any Autonomy usecase" rests on a single Lidar example; a direct test would be running the identical prompt sequence for a Radar or Camera failure and checking whether the resulting tree is both renderable and complete.
  • Editorial extension: if the approach scales, the bottleneck shifts from drawing diagrams to validating cut sets and gate logic, which is precisely the expertise that remains with functional-safety engineers.
  • Editorial extension: the success of error-feedback prompting suggests that LLMs can self-correct structured outputs when given concrete syntax errors, which may generalize to other diagram or model-based safety artifacts.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper explores the use of large language models (LLMs) — Gemini, ChatGPT, and Perplexity — to generate fault tree analysis (FTA) diagrams for a Lidar sensor failure in autonomous driving. The authors report that Perplexity, aided by PlantUML and iterative prompt engineering, can produce syntactically valid diagrams with AND/OR gates and additional event categories. The abstract claims that this "successfully shows the possibility to train existing Large Language models through Prompt Engineering for fault tree analysis for any Autonomy usecase aided with PlantUML tool." The paper includes code listings of the generated PlantUML and describes a workflow of feeding syntax errors back into the model to repair them.

Significance. If the central claim were fully supported, the paper would offer a useful low-cost approach to drafting fault trees early in the functional-safety workflow, potentially reducing manual effort and enabling tighter integration with textual requirements. The paper's concrete assets — the PlantUML code listings and the prompt-engineering loop for syntax-error repair — are reproducible and could serve as a starting point for more rigorous studies. However, the significance is currently limited because the paper does not validate the semantic correctness of the generated fault trees, and the single-example generalization to "any Autonomy usecase" is not supported. The work is best read as a preliminary feasibility demonstration of syntactic FTA diagram generation, not as a validated FTA methodology.

major comments (3)
  1. [Section VII.B.2 and Section IX] The central claim that the model "successfully" generates FTA is not supported because the logical content of the fault tree — event selection, gate types, completeness, and correctness — is never validated against a reference FTA, a known failure mode list, or a safety standard such as IEC 61025. The only evidence offered is that the PlantUML code renders without syntax errors (Section VIII, observation 3), which does not establish that the diagram is a valid fault tree. Section IX explicitly states that the model "needs sufficient training to ... ensure correctness as it modifies the UML code," contradicting the abstract's success claim. The authors should either provide a correctness validation (e.g., comparison with a reference FTA or expert review) or revise the claim to "generates syntactically valid PlantUML diagrams" rather than "successfully trains LLMs for fault tree analysis."
  2. [Section VII.B.2] The final diagram changes the logic from the all-OR semantics of the initial PlantUML version (Section VI.A, Listing 1, note "All connections represent OR gates") to AND gates for HardwareFailure and EnvironmentalFactors. No justification or source is given for why these particular combinations (e.g., "Laser emitter degradation" AND "Power supply issues") are necessary and sufficient. Since the gate logic is the core of any fault tree, this arbitrary shift without explanation undermines the claim that the generated diagram is a valid FTA. The authors should provide a rationale or a reference for each gate type.
  3. [Abstract and Section IX] The generalization "for any Autonomy usecase" is not supported by the single Lidar example presented in the paper. Section IX acknowledges "Current paper majorly focuses on one particular usecase for Lidar failure" and lists cameras and radars as future work. The claim should be scoped to the demonstrated Lidar sensor case unless additional examples are provided.
minor comments (5)
  1. [Throughout] Several cross-references are broken: "Code generated: ??" (Section VII.B.1), "?? is the code snippet" (Section VII.B.2), "Code snippet ?? and the figureII" (Section VII.B.2), and "figure4e" (Section V.2). These placeholders must be resolved before publication.
  2. [Section I.C] The sentence "Table2shows a list of basic questions" references a table that does not appear in the manuscript; either insert the table or remove the reference.
  3. [References] The reference list needs cleanup: reference [30] is a Bing video link without proper bibliographic information, and reference [21] is incomplete ("Single-shell Tank. AAS 00 seeettm data transmittal page").
  4. [Section IV.C] The five-item list of generic training strategies is only loosely connected to the paper's contribution and reads as padding; consider cutting it or citing a source.
  5. [Section V] The comparison of the three LLMs is anecdotal; a more systematic protocol (e.g., identical prompts, number of attempts, success criteria, and a rubric for evaluating the outputs) would strengthen the findings.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the FTA outputs are external LLM artifacts, and the paper's claims are empirical inferences rather than reductions to fitted inputs or self-cited results.

full rationale

The paper's central claim is that prompt-engineered LLMs can produce PlantUML-based fault tree drafts for a Lidar sensor failure. The evidence consists of externally generated diagrams and code from ChatGPT, Gemini, and Perplexity. There is no equation, fitted parameter, or quantity defined in terms of the claimed output; no load-bearing result is imported from the authors' own prior work; and the standardized-shape material is background reference, not a self-citation chain. The later code in Section VII.B.2 changes OR semantics to AND gates without reference-tree validation, and Section IX explicitly says the model 'needs sufficient training to ... ensure correctness as it modifies the UML code.' Those passages weaken the empirical support for the abstract's success claim and its 'any Autonomy usecase' generalization, but they are validity/correctness limitations, not circularity: the LLM output is not equivalent to the paper's input by construction. No specific circular step can therefore be quoted, so the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no mathematical parameters or new entities. Its load-bearing assumptions are domain-level: standards define correct FTA, Perplexity is a valid stand-in for LLMs, and one case generalizes.

assumptions (4)
  • domain assumption The ISO 26262 functional safety standard defines the relevant safety analysis context.
    Invoked in Section I-A to motivate FTA; the paper's evaluation assumes this standard is the correct lens.
  • domain assumption The gate symbols and event shapes described in Section IV represent industry-standard FTA.
    The paper judges LLM output against this description, without independently verifying that the LLM's diagram is semantically correct.
  • ad hoc to paper Perplexity is a representative LLM for GenAI code generation.
    The paper selects Perplexity after informal comparisons with Gemini and ChatGPT; no systematic benchmark justifies this choice.
  • ad hoc to paper A single Lidar failure case is representative of all Autonomy use cases.
    The conclusion generalizes from one sensor and one top event to 'any Autonomy usecase', which is a domain assumption not supported by data.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FTA generation using GenAI with an Autonomy sensor Usecase." pith.science (2026). https://pith.science/paper/62DYKSGA

@misc{pith2026241115007,
  author       = {Pith},
  title        = {Pith review of: FTA generation using GenAI with an Autonomy sensor Usecase},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/62DYKSGA}},
  note         = {Machine review of arXiv:2411.15007}
}
read the original abstract

Functional safety forms an important aspect in the design of systems. Its emphasis on the automotive industry has evolved significantly over the years. Till date many methods have been developed to get appropriate FTA(Fault Tree analysis) for various scenarios and features pertaining to Autonomous Driving. This paper is an attempt to explore the scope of using Generative Artificial Intelligence(GenAI) in order to develop Fault Tree Analysis(FTA) with the use case of malfunction for the Lidar sensor in mind. We explore various available open source Large Language Models(LLM) models and then dive deep into one of them to study its responses and provide our analysis. This paper successfully shows the possibility to train existing Large Language models through Prompt Engineering for fault tree analysis for any Autonomy usecase aided with PlantUML tool.

Figures

Figures reproduced from arXiv: 2411.15007 by the authors.

Figure 1
Figure 1. Common models in Generative AI domain data. These are used for generative tasks by conditioning the model on a specific point or a theme to generate new, relevant data This is majorly used in generating creative text formats like poems and scripts, translating languages and summarizing text content. 5. Reinforcement learning for Generative Tasks Here a reinforcement learning agent interacts with the surrounding envi… view at source ↗
Figure 3
Figure 3. Standard symbols used for FTA [26] of UML(Unified Modeling Language) code. 4. PlantUML is explored in its entirety to generate FTA diagrams. 5. Successful experiments open up scope for future work. IV. STANDARDIZED SHAPES FOR FTA Several standards documents and technical papers related to Fault Tree Analysis (FTA) have been published, outlining standard shapes and methodologies used in this analysis.[20] Here are so… view at source ↗
Figure 6
Figure 6. Perplexity generated initial FTA [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: ChatGPT generated initial FTA [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 7
Figure 7. Figure 7: Perplexity generated second FTA it more aligned with professional FTA practices. Next as we have seen in standard shapes, we do not use above signs but actual AND/OR gates to represent the transitions. Thus we requested it again to generate with AND/OR gates and got th…
Figure 5
Figure 5. Figure 5: ChatGPT generated final FTA We fine tuned our prompt to get a figure with gates [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 8
Figure 8. Figure 8: Perplexity generated final FTA [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: Basic symbols for PlantUML [33] VI. UTILISE CODE GENERATION CAPABILITIES OF GENAI MODELS TO GENERATE FTAS Like FTA, UML also has standard shapes illustrated in table 9 A. PlantUML Initial FTA The series of prompts provided to Perplexity to generate our first PlantUML d…
Figure 10
Figure 10. Figure 10: Plant UML generated FTA for autonomy left flowchart [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 11
Figure 11. Figure 11: Plant UML generated FTA for autonomy right flowchart [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]
Figure 12
Figure 12. Figure 12: Plant UML generated FTA with performance degradation added to events left part of flowchart [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: Plant UML generated FTA with performance degradation added to events right part of flowchart [PITH_FULL_IMAGE:figures/full_fig_p014_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

39 extracted references · 37 canonical work pages

  1. [1]

    Kapitonov

    Mikhail V . Kapitonov. Evaluation and analysis of risks in automotive industry. Transportation Research Pro- cedia, 61:556–560, 2022. XII International Conference on Transport Infrastructure: Territory Development and Sustainability (TITDS-XII)

    work page 2022

  2. [2]

    Iso 26262 functional safety–an approach for compliance readiness

    Isha Pathak and Bhushan Kothari. Iso 26262 functional safety–an approach for compliance readiness. Technical report, SAE Technical Paper, 2024

    work page 2024

  3. [3]

    Development of the iso 21448

    Adam Schnellbach and Gerhard Griessnig. Development of the iso 21448. In Systems, Software and Services Process Improvement: 26th European Conference, Eu- roSPI 2019, Edinburgh, UK, September 18–20, 2019, Proceedings 26, pages 585–593. Springer, 2019

    work page 2019

  4. [4]

    Avsc best practice for describing an operational design domain: Conceptual framework and lexicon

    Automated Vehicle Safety Consortium et al. Avsc best practice for describing an operational design domain: Conceptual framework and lexicon. SAE Industry Tech- nologies Consortia, 2020

    2020

  5. [5]

    Har- nessing generative ai for manufacturing innovation: Ap- plications and opportunities

    Mofeoluwa Jide-Jegede and Tomiwa Omotesho. Har- nessing generative ai for manufacturing innovation: Ap- plications and opportunities. In 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pages 568–572, 2024

    work page 2024

  6. [6]

    An introduction to neural networks

    Kevin Gurney. An introduction to neural networks. CRC press, 2018

    2018

  7. [7]

    The backpropagation algo- rithm

    Raul Rojas and Ra ´ul Rojas. The backpropagation algo- rithm. Neural networks: a systematic introduction, pages 149–182, 1996

    work page 1996

  8. [8]

    Swapdt: a method for short-time withering assessment of probability for drought tolerance in camellia sinensis validated by targeted metabolomics

    Christopher Nyarukowa, Robert Koech, Theodor Loots, and Zeno Apostolides. Swapdt: a method for short-time withering assessment of probability for drought tolerance in camellia sinensis validated by targeted metabolomics. Journal of plant physiology , 198:39–48, 2016

    work page 2016

Show all 39 references
  1. [9]

    Self-driving cars: Evaluation of deep learning techniques for object detection in different driving conditions

    Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, and Robert Slater. Self-driving cars: Evaluation of deep learning techniques for object detection in different driving conditions. SMU Data Science Review , 2(1):23, 2019

    2019

  2. [10]

    Natural lan- guage processing

    KR1442 Chowdhary and KR Chowdhary. Natural lan- guage processing. Fundamentals of artificial intelligence, pages 603–649, 2020

    2020

  3. [11]

    Predictive risk assessment of heart failure using hazop and qualitative risk analyses

    Temitope Alaba Oshin and Ayomide Josephine Tawose. Predictive risk assessment of heart failure using hazop and qualitative risk analyses. In 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG) , volume 1, pages 1–8, 2023

    2023

  4. [12]

    13, 2024

    2019 Oct. 13, 2024. [Online]. European standards, https://www.en-standard.eu/new-aiag-vda-fmea- handbook-failure-mode-and-effects-analysis/?, 2019

    2019

  5. [13]

    Harnessingai, 2023

    Joel Thurlby. Harnessingai, 2023

    2023

  6. [14]

    Review of the safety engineering techniques for a complex ship system

    Sunil Basnet, Osiris Alejandro Valdez Banda, Pentti Kujala, et al. Review of the safety engineering techniques for a complex ship system. In The 7th Asia Conference on Earthquake Engineering . ACEE, 2018

    2018

  7. [15]

    A function failure approach to fault tree analysis for automotive systems

    Felician Campean and Ed Henshall. A function failure approach to fault tree analysis for automotive systems. SAE International, pages 01–0846, 2008

    2008

  8. [16]

    Safety-critical failure analysis of industrial automotive airbag system using fmea and fta techniques

    M Ben Swarup and K Amaravathi. Safety-critical failure analysis of industrial automotive airbag system using fmea and fta techniques. International Journal of Ad- vanced Research in Computer Science , 5(5), 2014

    2014

  9. [17]

    i-sart: An intelligent assistant for safety analysis in radiation therapy

    Natalia Silvis-Cividjian, Yijing Zhou, Anastasia Sar- chosoglou, and Evangelos Pappas. i-sart: An intelligent assistant for safety analysis in radiation therapy. In BIOSTEC (2), pages 420–427, 2024

    2024

  10. [18]

    Unlocking the power of generative ai models and systems such as gpt-4 and chatgpt for higher education: A guide for students and lecturers

    Henner Gimpel, Kristina Hall, Stefan Decker, Torsten Eymann, Luis L ¨ammermann, Alexander M ¨adche, Max- imilian R ¨oglinger, Caroline Ruiner, Manfred Schoch, Mareike Schoop, et al. Unlocking the power of generative ai models and systems such as gpt-4 and chatgpt for higher ed...

    2023

  11. [19]

    Safety co-pilot: A system for autonomous vehicle to make decision safer and smarter

    Jiayi Lu, Shichun Yang, Runwu Shi, Yuyi Chen, and Yaoguang Cao. Safety co-pilot: A system for autonomous vehicle to make decision safer and smarter. In 2022 IEEE 25th International Conference on Intelligent Transporta- tion Systems (ITSC) , pages 1809–1816. IEEE, 2022

    2022

  12. [20]

    Effective static and dynamic fault tree analysis

    Ola B ¨ackstr¨om, Yuliya Butkova, Holger Hermanns, Jan Krˇc´al, and Pavel Kr ˇc´al. Effective static and dynamic fault tree analysis. In Computer Safety, Reliability, and Security: 35th International Conference, SAFECOMP 2016, Trondheim, Norway, September 21-23, 2016, Pro- cee...

    2016

  13. [21]

    Aas 00 seeettm data transmittal page

    Single-shell Tank. Aas 00 seeettm data transmittal page. NA, NA

  14. [22]

    A comparison of stpa and the arp 4761 safety assessment process

    Nancy Leveson, Chris Wilkinson, Cody Fleming, John Thomas, and Ian Tracy. A comparison of stpa and the arp 4761 safety assessment process. Massachusetts Institute of Technology, Cambridge, MA, 2014

    2014

  15. [23]

    Use of statistical considerations in establishing 16 design allowables for military handbook 5

    DONALD MOON, DONALD SHINN, and W ALTER HYLER. Use of statistical considerations in establishing 16 design allowables for military handbook 5. In Symposium on Deep Submergence Propulsion and Marine Systems , page 2522, 1966

    1966

  16. [24]

    Model-based approach for automatic generation of iec-61025 standard compliant fault trees, 2018

    Enrique Zornoza Moreno. Model-based approach for automatic generation of iec-61025 standard compliant fault trees, 2018

    2018

  17. [25]

    [On- line]

    United State Dept Of Transportation Aug 9, 2024. [On- line]. Fta report, https://www.transit.dot.gov/research- innovation/mobility-data-standards-and-specifications- interoperability-report-0267

    2024

  18. [26]

    13, 2024

    Ted Hessing Oct. 13, 2024. [Online]. Sixsigma fta, https://sixsigmastudyguide.com/fault-tree-analysis/, 2020

    2024

  19. [27]

    13, 2024

    OpenAI Oct. 13, 2024. [Online]. Chatgpt, https://chatgpt.com/

    2024

  20. [28]

    13, 2024

    GOOGLE Oct. 13, 2024. [Online]. Gemini, https://gemini.google.com/

    2024

  21. [29]

    13, 2024

    Denis Yarats Oct. 13, 2024. [Online] Aravind Srini- vas Andy Konwinski and Johnny Ho. Perplexity, https://www.perplexity.ai/

    2024

  22. [30]

    bing video, https://www.bing.com/videos/riverview/relatedvideo? q=research+links+to+do+fta+nalysis ‘i&’mid=1c7f6042d569d7fdd4af1c7f6042d569d7fdd4af‘i&’form =vire

    Bing. bing video, https://www.bing.com/videos/riverview/relatedvideo? q=research+links+to+do+fta+nalysis ‘i&’mid=1c7f6042d569d7fdd4af1c7f6042d569d7fdd4af‘i&’form =vire

  23. [31]

    [Online] John Wiley ‘I&’ Sons Inc

    2024. [Online] John Wiley ‘I&’ Sons Inc. Sept 5. wiley, https://onlinelibrary.wiley.com/doi/full/10.1002/qre.3271

    2024 doi

  24. [32]

    Towards uml and vdm support in the vs code environment

    Jonas Lund, Lucas Bjarke Jensen, Hugo Daniel Macedo, and Peter Gorm Larsen. Towards uml and vdm support in the vs code environment. In Proceedings of the 20th International Overture Workshop, pages 50–65, 2022

    2022

  25. [33]

    25, 2024

    CS Odessa Oct. 25, 2024. [Online]. Howtouml, https://www.conceptdraw.com/how-to-guide/diagrams- software-tools-for-design-uml-collaboration-diagram

    2024

  26. [34]

    [Online]

    Reliotech Oct 15, 2024. [Online]. Fta tool,https://www.fault-tree-analysis.com/

    2024

  27. [35]

    [Online]

    Gabriel Valiente Oct 15, 2024. [Online]. Mermaid tool, https://mermaid.live/

    2024

  28. [36]

    [Online]

    Various Contributors Oct 15, 2024. [Online]. yuml tool, https://yuml.me/

    2024

  29. [37]

    [Online]

    Various Contributors Oct 15, 2024. [Online]. nomnoml tool, https://nomnoml.com/

    2024

  30. [38]

    [Online]

    Various Contributors Oct 15, 2024. [Online]. kroki tool, https://kroki.io/. Sneha Sudhir Shetiya is a Senior IEEE Member and received her Maters degree in Electrical Engi- neering with a major in computer vision and Signal Processing from North Carolina State University, in

    2024

  31. [2021]

    Her work involves middleware topics for embedded development of autonomous driving stack, automotive diagnostics, systems engineering and functional safety

    She received her Bachelor’s degree in Elec- tronics and Communication Engineering from the Visvesvaraya Technological University, Karnataka, India, in 2014. Her work involves middleware topics for embedded development of autonomous driving stack, automotive diagnostics, system...

    2014

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.