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# Student/in - Survey für Fahrzeug AD - Software Architekturen

- **Company:** Deutsches Zentrum für Luft- und Raumfahrt e. V.
- **Location:** Stuttgart, Germany
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Data Mining, Middleware, Open Source Technology, Software Architecture, Red Hat Enterprise Linux, Zotero, Large Language Models, Information Technology, Baidu
- **Published:** September 20, 2026
- **Apply:** https://www.xing.com/jobs/stuttgart-student-survey-fahrzeug-ad-software-architekturen-157986255

## About the Role

* wissenschaftliches Hochschulstudium in Technischer BWL, Wirtschaftsingenieurwesen, Maschinenbau, Fahrzeugtechnik, Informatik, Robotik, Elektrotechnik, Mechatronik, Regelungstechnik, Luft- & Raumfahrt oder ähnlich
 * Interesse an automatisiertem Fahren, Software-Architekturen und Agentic AD/LLMs
 * Grundkenntnisse in Literaturrecherche & Zitieren (z. B. BibTeX/Zotero, systematisches Screening)
 * strukturiertes, analytisches Arbeiten, präzise Dokumentation (DE/EN)

## Description

Wir suchen dich im Team für eine systematische Survey-Arbeit zu Software-Architekturen im automatisierten Fahren - mit Fokus auf End-to-End, modulare Pipelines und Agentic AD (LLM-gestützte, tool-nutzende Agenten/Orchestrierungen). Du entwickelst eine Taxonomie der Architekturen, analysierst Open-Source-Stacks (z. B. Autoware, Baidu Apollo, ggf. S-Core/Red Hat-Ökosystem) anhand von Dokumentation, Releases und Repositories und leitest Trends, Gaps und Empfehlungen ab., * Thema schärfen, Forschungsfragen formulieren

 * systematische Literatursuche (IEEE, ACM, arXiv, Google Scholar), Screening & Datenextraktion
 * Entwicklung einer Taxonomie: E2E vs. Modular vs. Agentisch (Rollen, Orchestrierung, Tool-Use, Safety/Runtime)
 * Open-Source-Stacks vergleichen: Architektur-Layer, Middleware (z. B. ROS 2/CyberRT), APIs, Roadmaps, Releases, Lizenz/Governance, Community-Metriken, Reifegrad
 * Agentic AD kartieren: Agent-Rollen (Planner, Safety-Critic/Verifier, Perception-Narrator, Controller-Designer), Kommunikationsmuster, Wissenseinbindung (RAG), Integrationspunkte in bestehende Stacks
 * Benchmark-/Datensatz-Landschaft für relevante Szenarien (z. B. Parken/Docking) zusammentragen
 * Evidenzsynthese, Vergleichstabellen & Visualisierungen erstellen; Guidelines & Research Gaps ableiten
 * wissenschaftliches Schreiben (DE/EN) und Abschlusspräsentation


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