[{"data":1,"prerenderedAt":1072},["ShallowReactive",2],{"/en-us/the-source/ai/why-automotive-software-development-needs-human-centered-ai/":3,"footer-en-us":53,"the-source-navigation-en-us":361,"the-source-newsletter-en-us":388,"why-automotive-software-development-needs-human-centered-ai-article-hero-category-en-us":400,"why-automotive-software-development-needs-human-centered-ai-the-source-gated-asset-en-us":424,"why-automotive-software-development-needs-human-centered-ai-category-en-us":439,"why-automotive-software-development-needs-human-centered-ai-the-source-resources-en-us":451,"why-automotive-software-development-needs-human-centered-ai-article-hero-author-en-us":491},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":17,"content":21,"_id":47,"_type":48,"title":7,"_source":49,"_file":50,"_stem":51,"_extension":52},"/en-us/the-source/ai/why-automotive-software-development-needs-human-centered-ai","ai",false,"",{"layout":9,"type":10,"template":11,"slug":12,"articleType":13,"category":5,"author":14,"featured":15,"gatedAsset":16},"the-source","article","TheSourceArticle","why-automotive-software-development-needs-human-centered-ai","Regular","lee-faus",true,"source-lp-transform-automotive-devops-secure-fast-future-ready",{"title":18,"description":19,"ogImage":20},"Why automotive software development needs human-centered AI","Learn why balancing AI assistance with human expertise is crucial for automotive embedded systems development and creating competitive advantages.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463704/u3dshy4qn6rtrklfalx7.png",{"title":18,"date":22,"description":19,"timeToRead":23,"image":24,"keyTakeaways":26,"articleBody":30,"faq":31},"2025-06-02","6 min read",{"config":25},{"src":20},[27,28,29],"AI in automotive embedded software development works best as a Level 2 assistant, meaning human expertise remains essential for effective embedded development in vehicles.","The right human-AI balance varies across different automotive software domains; teams that find the right balance between AI assistance and human expertise will gain competitive advantages.","Creating effective human-AI partnerships requires intentional processes such as mandatory human review checkpoints for safety-critical systems.","Software is an essential part of modern automobiles. This year, the lines of code in the average car are expected to reach [650 million](https://www.statista.com/statistics/1370978/automotive-software-average-lines-of-codes-per-vehicle-globally/), an increase from 200 million in 2020. What’s more, we’re seeing a shift from distributed architectures for vehicle firmware toward zonal architectures with central high-performance computers (HPCs). All of this creates complexity and novel software challenges.\n\nEmbedded systems developers are trying to adapt to this complexity. At the same, market pressures are forcing them to accelerate their development processes and ship innovation faster.\n\nArtificial intelligence (AI) can help address these challenges, but its implementation raises important questions. To what degree should AI tools autonomously generate and review code in automotive embedded systems? How much human oversight is advisable? Drawing from the automotive industry's vocabulary, I propose that embedded development requires Level 2 AI assistance - at least right now.\n\n## Understanding Level 2 automation for AI in embedded development\nIn automotive driving automation, [Level 2 systems](https://www.sae.org/blog/sae-j3016-update) represent partial automation: a carefully balanced human-machine collaboration. These systems can help control steering, acceleration, and braking in specific scenarios, but the driver must stay engaged. They must monitor the environment and be ready to take control at any moment. The human remains legally responsible for the vehicle's operation and must supervise the automation continually. In contrast, Level 4-5 systems aim to operate with minimal or no human oversight in defined conditions.\n\nThis framework provides a useful analogy for AI in embedded development. Current AI tools excel at providing suggestions and automating routine tasks, much like Level 2 driver assistance. They can suggest code, help with testing, and identify potential issues. However, their contextual understanding has limitations. Given the high stakes of automotive embedded systems, combining AI's capabilities with human wisdom and oversight is best.\n\n## Why AI excels as a development assistant\nAI shows remarkable capabilities across numerous areas of embedded development. Here are just a few examples from the growing list of applications:\n\nFirst, AI can [generate and complete code](https://docs.gitlab.com/user/project/repository/code_suggestions/) for common patterns in C/C++, reducing developers' time spent on routine programming tasks. And if prompted correctly, AI can respect embedded-specific constraints like memory limitations and hardware interfaces.\n\nSecond, AI can [generate tests](https://docs.gitlab.com/user/gitlab_duo_chat/examples/#write-tests-in-the-ide) that you can run on cloud-based ARM CPUs or virtual hardware. This helps teams \"shift left\" in testing their firmware and catch issues earlier in development when they're less expensive to fix. It also helps identify edge cases you might have otherwise overlooked.\n\nThird, AI can help [accelerate the remediation of security vulnerabilities](https://docs.gitlab.com/user/application_security/vulnerabilities/#explaining-a-vulnerability) in your code. AI tools can help interpret security findings from your security scanners. They can even suggest potential approaches to address issues, supporting development teams as they work to meet cybersecurity requirements in this highly regulated space. AI thus helps expedite remediation.\n\nBeyond these examples, AI is increasingly valuable for [root cause analysis](https://docs.gitlab.com/user/gitlab_duo_chat/examples/#troubleshoot-failed-cicd-jobs-with-root-cause-analysis) of complex issues, comprehensive [code reviews](https://docs.gitlab.com/user/project/merge_requests/duo_in_merge_requests/#have-gitlab-duo-review-your-code), automated [code refactoring](https://about.gitlab.com/blog/2024/08/26/refactor-code-into-modern-languages-with-ai-powered-gitlab-duo/) for optimization, [explaining](https://docs.gitlab.com/user/project/merge_requests/changes/#explain-code-in-a-merge-request) complex legacy code, and providing conversational assistance through [AI chat capabilities](https://docs.gitlab.com/user/gitlab_duo_chat/). As AI evolves, so will the ways in which it assists embedded development teams.\n\n## The essential human element\nThough these AI capabilities are quite powerful, they cannot - and should not - replace human expertise. Embedded developers bring domain knowledge that spans both software and hardware domains, understanding not just how to code, but how that code interacts with physical components under varying conditions.\n\nMoreover, embedded developers understand the intricate relationships between different vehicle subsystems. Far from replacing such expertise, AI must integrate with human beings' contextual knowledge.\n\nHumans also bring creativity and innovation to solving unique automotive challenges. When faced with conflicting requirements or novel problems, human engineers draw on experience and intuition that AI simply doesn't possess.\n\nThe human-centered approach is critical in automotive development, where safety and reliability cannot be compromised. Just as a driver must remain alert and ready to take control of a Level 2 automated vehicle, developers must maintain ultimate responsibility for AI-generated code. While valuable, AI suggestions require expert validation. Developers must review and verify that proposed solutions solve the problem correctly within the specific automotive context.\n\nThis human oversight becomes even more critical when considering the consequences of errors. In enterprise software, a bug might cause inconvenience; in automotive systems, it could potentially impact passenger safety. Developers bring ethical judgment and a holistic understanding of the operating environment that AI currently lacks. They can anticipate edge cases based on real-world driving conditions and evaluate AI recommendations against their practical experience with actual vehicle systems.\n\n## Creating an effective human-AI partnership\nBelow are some initial approaches to consider as you begin building productive partnerships between developers and AI.\n\nStart by identifying specific high-volume, low-risk tasks where AI can provide immediate value: unit test generation for non-safety-critical components, documentation updates, and routine code standardization are excellent entry points.\n\nImplement a tiered approach to AI integration based on system criticality. For infotainment or connectivity systems, teams might leverage more autonomous AI assistance. For safety-related systems, establish mandatory human review checkpoints with structured approval workflows. Create clear guidelines on which code components require senior engineer review versus those where junior developers can approve AI suggestions with minimal oversight.\n\nReview processes also need adaptation. Rather than having humans review AI-generated code in isolation, teams should implement collaborative workflows where AI assists with the review itself, highlighting potential issues for human evaluation. Consider adopting structured prompting techniques. For example, have developers specify constraints like memory requirements, coding standards, or performance parameters before generating AI suggestions.\n\nThese examples represent starting points for effective human-AI collaboration in embedded development.\n\n## Looking to the future\nThe human-AI partnership will evolve across different automotive domains as AI capabilities advance. Teams should prepare by focusing on higher-value skills that complement AI capabilities, such as systems architecture, integration expertise, and hardware-software design.\n\nThe teams that succeed will find the right balance, leveraging AI to handle routine tasks while keeping humans at the center of the development process. This is the path to realizing AI's productivity promise.\n\n_I'll be discussing topics like this and more with Dr. Felix Kortmann of Ignite by FORVIA HELLA in a webinar on June 11. The webinar will be on “Building the Future of Automotive Software.” Join us to learn how to effectively balance AI assistance with human expertise in your embedded development teams. [Register here](https://page.gitlab.com/webcasts-jun11-gitlab-ignite-by-foriva-hella-emea-amer.html?utm_medium=referral&utm_source=gmail&utm_campaign=20250611_global_cmp_webcast_speedsecurity_en_&utm_content=salespromo_x_auto)._",[32,35,38,41,44],{"header":33,"content":34},"What is Level 2 AI assistance in automotive software development?","Level 2 AI refers to a collaborative human-AI model where AI supports tasks like code generation and testing, but developers retain oversight and responsibility. Like Level 2 driving automation, the human stays in control, ensuring contextual accuracy and safety.",{"header":36,"content":37},"How does the role of AI differ across various automotive software domains?","AI adds value across all domains, but oversight levels vary. Safety-critical systems require stricter human validation, while infotainment systems allow more autonomous AI use. Teams should tailor AI workflows based on system risk and regulatory requirements.",{"header":39,"content":40},"How can teams establish effective AI review processes for embedded code?","Teams should use a tiered review structure. AI can perform initial quality checks — flagging syntax issues or common errors — while human experts review critical code sections and system interfaces. Clear guidelines should define when AI-generated suggestions require additional human verification or senior engineer approval to ensure safe integration within embedded systems.",{"header":42,"content":43},"What skills should embedded developers focus on as AI capabilities expand?","Embedded developers should deepen their understanding of systems architecture, hardware-software integration, and domain-specific safety requirements. Skills in prompt engineering and AI collaboration, such as framing effective prompts and interpreting model outputs, are also increasingly important. These competencies help developers remain effective evaluators and collaborators alongside AI systems.",{"header":45,"content":46},"How can AI help address the shortage of embedded software expertise in the automotive industry?","AI reduces the burden on experienced engineers by automating routine development tasks like boilerplate coding, unit testing, and documentation. This allows senior engineers to focus on high-impact projects and mentoring. At the same time, AI tools help junior developers ramp up faster by guiding them through embedded-specific best practices, accelerating onboarding and reducing skill barriers.","content:en-us:the-source:ai:why-automotive-software-development-needs-human-centered-ai:index.yml","yaml","content","en-us/the-source/ai/why-automotive-software-development-needs-human-centered-ai/index.yml","en-us/the-source/ai/why-automotive-software-development-needs-human-centered-ai/index","yml",{"_path":54,"_dir":55,"_draft":6,"_partial":6,"_locale":7,"data":56,"_id":357,"_type":48,"title":358,"_source":49,"_file":359,"_stem":360,"_extension":52},"/shared/en-us/main-footer","en-us",{"text":57,"source":58,"edit":64,"contribute":69,"config":74,"items":79,"minimal":349},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":59,"config":60},"View page 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During her tenure with Google, she was directly responsible for the reliability, performance, and efficiency of all of Google's billion-user products and infrastructure.\n\nA long-time advocate for women in technology, Farmer earned a B.S. in Computer Science at the University of New Orleans, where she established two scholarships to help level the playing field for inclusion and empowerment in technology.",{"altText":979,"config":987},{"src":988},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463377/udmzbjjr5xrcrffdlphx.webp",{"componentName":513},"content:en-us:the-source:authors:sabrina-farmer.yml","en-us/the-source/authors/sabrina-farmer.yml","en-us/the-source/authors/sabrina-farmer",{"_path":994,"_dir":494,"_draft":6,"_partial":6,"_locale":7,"config":995,"seo":997,"content":999,"_id":1009,"_type":48,"title":998,"_source":49,"_file":1010,"_stem":1011,"_extension":52},"/en-us/the-source/authors/sandra-gittlen",{"layout":9,"type":496,"slug":996},"sandra-gittlen",{"title":998},"Sandra Gittlen",[1000,1008],{"componentName":502,"componentContent":1001},{"config":1002,"name":998,"role":1004,"headshot":1005},{"gitlabHandle":1003},"sgittlen","Managing Editor, Blog",{"altText":998,"config":1006},{"src":1007},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463466/r7ckb9h2zr4c2rsz3zlm.png",{"componentName":513},"content:en-us:the-source:authors:sandra-gittlen.yml","en-us/the-source/authors/sandra-gittlen.yml","en-us/the-source/authors/sandra-gittlen",{"_path":1013,"_dir":494,"_draft":6,"_partial":6,"_locale":7,"config":1014,"seo":1016,"content":1018,"_id":1026,"_type":48,"title":1017,"_source":49,"_file":1027,"_stem":1028,"_extension":52},"/en-us/the-source/authors/sharon-gaudin",{"layout":9,"type":496,"slug":1015},"sharon-gaudin",{"title":1017},"Sharon Gaudin",[1019,1025],{"componentName":502,"componentContent":1020},{"config":1021,"name":1017,"headshot":1022},{},{"altText":1017,"config":1023},{"src":1024},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463475/h6j4nnvykzyfzgvl7txb.webp",{"componentName":513},"content:en-us:the-source:authors:sharon-gaudin.yml","en-us/the-source/authors/sharon-gaudin.yml","en-us/the-source/authors/sharon-gaudin",{"_path":1030,"_dir":494,"_draft":6,"_partial":6,"_locale":7,"config":1031,"seo":1033,"content":1035,"_id":1047,"_type":48,"title":1034,"_source":49,"_file":1048,"_stem":1049,"_extension":52},"/en-us/the-source/authors/stephen-walters",{"layout":9,"type":496,"slug":1032},"stephen-walters",{"title":1034},"Stephen Walters",[1036,1046],{"componentName":502,"componentContent":1037},{"config":1038,"name":1034,"role":1041,"bio":1042,"headshot":1043},{"gitlabHandle":1039,"linkedInProfileUrl":1040},"swalters1","https://www.linkedin.com/in/1stephenwalters/","Field CTO, GitLab","Stephen Walters is Field CTO for GitLab. Stephen has been in the IT industry for over 30 years. He is an extensively experienced subject matter expert in Value Stream Management, DevSecOps, DevOps, ALM, SDLC and IT4IT, with management and consultancy experience across end-to-end IT disciplines. Currently also operating as an Ambassador for the DevOps Institute and an Influencer in the Value Stream Management Consortium, he is interested in all things DevOps. Stephen is a co-author of the Value Stream Reference Architectures white paper and is currently pursuing further research into Value Stream Management, Organizational Architecture and AI.",{"altText":1034,"config":1044},{"src":1045},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463392/g6ktk5qb4vcqc9wqjlf9.jpg",{"componentName":513},"content:en-us:the-source:authors:stephen-walters.yml","en-us/the-source/authors/stephen-walters.yml","en-us/the-source/authors/stephen-walters",{"_path":1051,"_dir":494,"_draft":6,"_partial":6,"_locale":7,"config":1052,"seo":1054,"content":1056,"_id":1068,"_type":48,"title":1069,"_source":49,"_file":1070,"_stem":1071,"_extension":52},"/en-us/the-source/authors/taylor-mccaslin",{"layout":9,"type":496,"slug":1053},"taylor-mccaslin",{"title":1055},"Taylor McCaslin",[1057,1067],{"componentName":502,"componentContent":1058},{"config":1059,"name":1055,"role":1062,"bio":1063,"headshot":1064},{"gitlabHandle":1060,"linkedInProfileUrl":1061},"tmccaslin","https://www.linkedin.com/in/taylormccaslin/","Group Manager, Product - Data Science","Taylor McCaslin is the Product Lead for AI/ML at GitLab, where he is responsible for leading the team of product managers who manage the AI Powered and ModelOps stage groups and sets the vision and direction for how to empower GitLab users to leverage data science as part of their DevOps program. Prior to joining GitLab, he held positions at Indeed, Duo Security, and WP Engine.",{"altText":1055,"config":1065},{"src":1066},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463427/trfasilaeasosxfcxmsm.jpg",{"componentName":513},"content:en-us:the-source:authors:taylor-mccaslin.yml","Taylor Mccaslin","en-us/the-source/authors/taylor-mccaslin.yml","en-us/the-source/authors/taylor-mccaslin",1751548567045]