AUTOSAR: Die Grundlage für ein softwaregesteuertes Cockpit
Close-up view of modular software components arranged in a grid, illustrating the complexity and precision of AUTOSAR architecture for digital cockpits.
An intricate array of AUTOSAR-compliant software components, representing the modular and standardised approach to digital cockpit development.

In Kürze

  • AUTOSAR (AUTomotive Open System ARchitecture) gilt als Standard in der automobilen Softwareentwicklung und bietet einen einheitlichen Rahmen, um die zunehmende Komplexität in digitalen Cockpits zu bewältigen.
  • Der modulare Ansatz, die Plattformunabhängigkeit und der Fokus auf Interoperabilität erleichtern die effiziente Entwicklung, Integration und Validierung von Cockpit-Softwarekomponenten.
  • Acsia bietet umfassende AUTOSAR-Lösungen an, die unser umfassendes Fachwissen nutzen, um den Lebenszyklus Ihrer Cockpit-Entwicklung zu optimieren und Ihnen ein innovatives Erlebnis im Fahrzeug zu bieten.

Die Automobilindustrie durchläuft einen Paradigmenwechsel, da softwaredefinierte Fahrzeuge (SDVs) zur Norm werden. Das digitale Cockpit, ein zentraler Knotenpunkt für die Mensch-Maschine-Interaktion (HMI), die Fahrzeugsteuerung und fortschrittliche Fahrerassistenzsysteme (ADAS), steht bei diesem Wandel an vorderster Front. Da die Komplexität der Cockpit-Software weiter zunimmt, ist ein strukturierter und standardisierter Ansatz wie AUTOSAR unerlässlich.

AUTOSAR entmystifizieren

AUTOSAR ist eine globale Partnerschaft zwischen führenden Automobilherstellern, Zulieferern und Technologieunternehmen, die sich der Definition offener Standards für E/E-Architekturen (Elektrik/Elektronik) im Automobil verschrieben hat. Schauen wir uns die wichtigsten Architekturprinzipien an, die AUTOSAR ideal für das digitale Cockpit machen:

  • Modulares Design: AUTOSAR fördert die Erstellung von in sich geschlossenen Softwarekomponenten (SWCs), die bestimmte Funktionen kapseln. Dieser modulare Ansatz ermöglicht eine effiziente Entwicklung, Wiederverwendung und unabhängige Tests einzelner SWCs, wodurch die Gesamtkomplexität des Systems reduziert wird.
  • Hardware-Abstraktion: Die AUTOSAR-Laufzeitumgebung (RTE) bietet eine Abstraktionsebene zwischen den SWCs und der zugrunde liegenden Hardware. Diese Entkopplung erleichtert die Portabilität von Software über verschiedene Fahrzeugplattformen hinweg, rationalisiert den Entwicklungsprozess und ermöglicht eine kostengünstige Skalierbarkeit.
  • Standardisierte Kommunikation: Der Virtual Functional Bus (VFB) ist ein zentrales AUTOSAR-Konzept, das die Kommunikation zwischen SWCs über standardisierte Schnittstellen ermöglicht, unabhängig von deren physischem Standort oder der zugrunde liegenden Implementierung. Dies fördert die Interoperabilität, vereinfacht die Integration und reduziert das Risiko von Fehlern aufgrund von Fehlkommunikation.

AUTOSAR: Bewältigung der Komplexität im Cockpit

Das digitale Cockpit ist ein Paradebeispiel dafür, wo die Stärken von AUTOSAR wirklich zum Tragen kommen:

  • Nahtlose Integration: Die Integration neuer Funktionen, egal ob es sich um eine Navigations-App eines Drittanbieters oder ein hochmodernes Fahrerüberwachungssystem handelt, wird durch das AUTOSAR-Framework vereinfacht. Klare Schnittstellen und standardisierte Datenaustauschmechanismen vereinfachen den Prozess und reduzieren die Entwicklungszeit und das Fehlerpotenzial.
  • Beschleunigte Entwicklung: Die Möglichkeit, bereits vorhandene AUTOSAR-konforme SWCs von verschiedenen Anbietern zu nutzen, ermöglicht es den Automobilherstellern, sich auf differenzierende Funktionen zu konzentrieren, was zu einer schnelleren Markteinführung und geringeren Entwicklungskosten führt.
  • Anpassungsfähig für morgen: Die modulare Architektur von AUTOSAR stellt sicher, dass es flexibel und kompatibel mit neuen Technologien bleibt. Ob es um die Integration von Cloud-Konnektivität, fortschrittlichen Algorithmen für maschinelles Lernen oder neuen HMI-Paradigmen geht, AUTOSAR bietet eine Grundlage für Innovationen.

Von klassisch zu adaptiv: Die Entwicklung von AUTOSAR

AUTOSAR hat sich weiterentwickelt, um den unterschiedlichen Anforderungen gerecht zu werden:

  • Klassisches AUTOSAR: Ideal für tief eingebettete Systeme mit Echtzeitbeschränkungen. Es eignet sich hervorragend für Anwendungen wie Antriebsstrangsteuerung und Fahrwerkssysteme.
  • Adaptive AUTOSAR: Diese Architektur ist auf Hochleistungs-Computing-Umgebungen zugeschnitten und unterstützt dynamische Anwendungen, serviceorientierte Strukturen und flexible Entwicklungsansätze. Sie eignet sich besonders für komplexe Infotainment-Systeme und neue Funktionen für das autonome Fahren im digitalen Cockpit.

Reale Vorteile für Automobilhersteller

Für diejenigen, die die Zukunft des Cockpits gestalten, bringt AUTOSAR greifbare Vorteile mit sich:

  • Kosteneffizienz: Die Wiederverwendung von Software, gestraffte Entwicklungsprozesse und die Zusammenarbeit über die gesamte Lieferkette hinweg tragen alle zur Senkung der Entwicklungs- und Wartungskosten bei.
  • Qualität und Sicherheit: AUTOSAR setzt sich für gründliche Testprotokolle ein, um die Softwarequalität zu verbessern und die Wahrscheinlichkeit von kritischen Sicherheitsfehlern zu minimieren. Es steht im Einklang mit Industriestandards wie ISO 26262 für funktionale Sicherheit.
  • Entfesselte Innovation: Durch die Vereinfachung der Integration und die Bereitstellung einer soliden Grundlage setzt AUTOSAR wertvolle Ressourcen für die Automobilhersteller frei, damit sie sich auf bahnbrechende Funktionen und Technologien konzentrieren können, die ihre digitalen Cockpits auszeichnen.

Acsia: Ihr AUTOSAR-Partner

Acsia verfügt über ein Team erfahrener AUTOSAR-Experten mit umfangreicher Erfahrung in:

  • AUTOSAR-Integration: Integrieren Sie neue Funktionen und SWCs nahtlos in Ihre bestehenden AUTOSAR-basierten Architekturen.
  • Entwicklung kundenspezifischer Software-Komponenten: Entwerfen und entwickeln Sie maßgeschneiderte SWCs, die Ihre individuellen Cockpit-Anforderungen erfüllen.
  • Optimierung und Tests: Stellen Sie die optimale Leistung und Zuverlässigkeit Ihrer AUTOSAR-basierten Systeme durch rigorose Tests und Validierung sicher.

Da die Automobilindustrie immer schneller auf eine Zukunft zusteuert, die von intelligenten, vernetzten und nutzerzentrierten Fahrzeugen bestimmt wird, ist die Einführung von Standards wie AUTOSAR nicht länger optional – sie ist unerlässlich, um die Komplexität zu bewältigen, die Sicherheit zu gewährleisten und kontinuierliche Innovationen im digitalen Cockpit zu ermöglichen. Mit fundiertem Fachwissen und einer nachgewiesenen Erfolgsbilanz steht Acsia bereit, Sie bei jedem Schritt auf Ihrem Weg zu unterstützen.

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AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

 

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

Context

Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

  • Manual log analysis is slow, error-prone, and requires deep expertise in both the system and its environment.
  • Critical issues can be missed or misdiagnosed, leading to longer downtimes and higher costs.
  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

Challenge

Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
  • Provide actionable insights that developers can use immediately.

Goal

Deliver a working prototype that:

  • Operates on sample log data.
  • Produces insights that are accurate, usable, and easy to interpret.
  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
  • Visualization/dashboard (if possible) for quick triage.

Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
  • Improved reliability of complex software systems.
  • Scalable approach that can be extended across industries (finance, automotive, telecom, healthcare).
AH2025/PS03 | AI/ML

Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

  • Current in-car experiences are one-size-fits-all, failing to account for individual preferences or needs.
  • Manual adjustments while driving can be distracting and unsafe.
  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

  • Driver profile (age, preferences, past behaviour).
  • Calendar & journey type (work commute, leisure trip, urgent travel).
  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
  • New value proposition for automakers: cars as intelligent, personalized environments, not just vehicles.
AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • Predict risks and delays using historical patterns and live progress signals.
  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
  • Resource allocation map showing workload distribution across the team.
  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
  • Better stakeholder communication → clear, automated updates.
  • Scalable for enterprises → can be deployed across multiple automotive software teams.
AH2025/PS01 | AI/ML

Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.