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Die Apps, die das digitale Cockpit antreiben: Erkundung der Anwendungsebene
High-tech digital cockpit interface showcasing various in-car applications developed by Acsia, enhancing the driving experience with intuitive controls and real-time information.
Acsia designs advanced in-car applications that power the digital cockpit, delivering a seamless and user-friendly driving experience.

In Kürze

  • In der Anwendungsschicht eines digitalen Cockpits befindet sich die Software, die direkt mit dem Benutzer interagiert. Dazu gehören Navigation, Unterhaltung und fahrzeugspezifische Apps.
  • Bei der Entwicklung von Apps für das Auto muss sorgfältig darauf geachtet werden, dass die Sicherheit im Vordergrund steht und ein nahtloses Benutzererlebnis gewährleistet ist.
  • Acsia hat nachweislich Erfahrung in der Entwicklung intuitiver, ansprechender und ablenkungsarmer Anwendungen für das digitale Cockpit.

Stellen Sie sich Ihr digitales Cockpit wie einen leistungsstarken Computer mit schlanken Displays und intuitiven Schnittstellen vor. Genau wie der Desktop oder der Startbildschirm auf Ihrem Computer oder Smartphone ist die Anwendungsebene das Herzstück dieses Systems. Hier finden Sie die Apps, die Ihnen die Funktionen, die Unterhaltung und die Informationen liefern, die Ihr Fahrerlebnis verändern. Lassen Sie uns in die Welt der Apps im Auto eintauchen und erfahren Sie, wie sie die Zukunft des Cockpits prägen.

Was ist die Anwendungsschicht?

Die Software, die ein digitales Cockpit steuert, ist komplex und vielschichtig. Stellen Sie sich das wie ein mehrstöckiges Gebäude vor:

  • Das Fundament: Die unteren Ebenen sind wie die strukturelle Unterstützung des Gebäudes. Dazu gehören das Betriebssystem (wie Linux oder Android), die Middleware und die Treiber – die Software “hinter den Kulissen”, die die Hardware steuert und die Kommunikation ermöglicht.
  • Die dem Benutzer zugewandte Ebene: Die Anwendungsebene ist wie das oberste Stockwerk, in dem die Nutzer leben und arbeiten. Es ist der Teil, mit dem Sie direkt interagieren. Navigations-Apps, Musik-Streaming-Dienste und Apps, die die Einstellungen Ihres Autos steuern, “leben” alle in dieser Ebene.

Arten von Apps, die in modernen Cockpits zu finden sind

Die Anwendungsebene eröffnet eine Welt der Möglichkeiten in Ihrem Auto. Hier sind einige der wichtigsten Kategorien von Anwendungen, die Sie finden:

  • Navigation und Kartierung: Vorbei sind die Zeiten, in denen Sie Papierkarten ausbreiten oder sich auf ein separates GPS-Gerät verlassen mussten. Moderne Navigations-Apps im Auto nutzen Echtzeit-Verkehrsdaten, dynamische Routenoptimierung und integrieren sogar Online-Datenbanken mit Points of Interest, um Sie effizient ans Ziel zu bringen.
  • Medien und Unterhaltung: Ihr Auto wird zu einer Erweiterung Ihres digitalen Lebens. Streaming-Dienste bringen eine praktisch unbegrenzte Bibliothek von Musik, Podcasts und Hörbüchern direkt auf Ihr Armaturenbrett. Je nach Fahrzeug und Sicherheitsbestimmungen können die Passagiere sogar Videoinhalte streamen, wenn sie geparkt sind.
  • Fahrzeugfunktionen: Viele physische Tasten und Knöpfe werden durch digitale Schnittstellen ersetzt. Mit speziellen Apps innerhalb der Anwendungsebene können Sie die Klimaeinstellungen steuern, die Fahrmodi anpassen, die Umgebungsbeleuchtung personalisieren und mit ein paar Fingertipps oder Sprachbefehlen auf andere fahrzeugspezifische Funktionen zugreifen.
  • Fahrzeugdiagnose und -status: Informieren Sie sich über den Zustand Ihres Fahrzeugs mit Apps, die in Echtzeit Informationen über Reifendruck, Kraftstoff- oder Batteriestand und den allgemeinen Systemstatus anzeigen. Einige Systeme bieten sogar proaktive Wartungswarnungen, die Sie über mögliche Probleme informieren, bevor Sie gestrandet sind.
  • Produktivität (mit Vorsicht zu genießen): Dies ist ein eher umstrittener Bereich. Je nach Philosophie des Autoherstellers können einige digitale Cockpits einfache Kalender-Apps, vereinfachte E-Mail-Programme oder Voice-to-Text-Funktionen integrieren. Denken Sie daran, dass die Sicherheit immer an erster Stelle stehen sollte. Funktionen, die die Aufmerksamkeit des Fahrers zu sehr beanspruchen, können zu einer gefährlichen Ablenkung werden.

Die Herausforderung, für den Fahrer zu entwerfen

Die Entwicklung von Apps, die sowohl nützlich als auch sicher in der Umgebung eines Autos sind, ist eine einzigartige Herausforderung. Softwareunternehmen für die Automobilindustrie müssen Prioritäten setzen:

  • Minimierung von Ablenkungen: Apps im Auto brauchen große Symbole, klare Schriftarten, vereinfachte Menüs und eine intelligente Verwendung von Sprachbefehlen. Funktionen, die dem Fahrer zu viel visuelle oder mentale Aufmerksamkeit abverlangen, sind potenziell gefährlich.
  • Nahtlose Integration: Apps sollten einwandfrei mit anderen Cockpit-Elementen zusammenarbeiten. Sprachbefehle, Bedienelemente am Lenkrad und die Möglichkeit, Informationen gemeinsam zu nutzen (z. B. Navigationsanweisungen, die im Kombiinstrument angezeigt werden), verbessern das Benutzererlebnis.
  • Die Plattformfrage: Die Automobilhersteller ringen mit der Entscheidung zwischen offenen App-Plattformen (wie bei unseren Smartphones) und streng kuratierten Umgebungen, in denen jede App sorgfältig geprüft wird. Offenheit bietet Flexibilität, birgt aber potenzielle Sicherheits- und Kompatibilitätsrisiken.

Acsia: Die Zukunft der In-Car Apps

Acsia kennt die besonderen Anforderungen bei der Entwicklung von Anwendungen für das digitale Cockpit. Hier erfahren Sie, wie wir sicherstellen, dass unsere App-Lösungen hervorragend sind:

  • Sicherheit geht vor: Wir priorisieren ablenkungsbewusste Designprinzipien. Unsere Apps für das Auto werden mit einem klaren Verständnis für die Bedürfnisse und Einschränkungen des Fahrers entwickelt.
  • Benutzerfreundlichkeit (UX): Intuitive Benutzeroberflächen, kontextbewusstes Design und der Fokus auf Personalisierung sind die Markenzeichen unseres Anwendungsentwicklungsprozesses.
  • Zukunftsorientiert: Wir erforschen, wie neue Technologien wie Augmented Reality (AR) und fortschrittliche Personalisierung das App-Erlebnis im Auto verbessern können, ohne die Sicherheit zu beeinträchtigen.

Wir bei Acsia entwickeln nicht einfach nur Apps – wir gestalten die Art und Weise neu, wie Fahrer und Passagiere mit ihren Fahrzeugen in Verbindung treten, eine intuitive Interaktion nach der anderen.

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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

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Challenge

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

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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.
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  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
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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.
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Challenge

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

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  • 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.
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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.

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Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
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Goal

Deliver a working prototype that:

  • Operates on sample log data.
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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).
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  • 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.
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  • 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.
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  • 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.
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  • 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).
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  • 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.
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  • 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.