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Erschließen Sie das Potenzial Ihres Fahrzeugs mit Telematik
Interior view of a car dashboard with a high-tech telematics system display, emphasising real-time diagnostics and connectivity.
Advanced telematics system in a modern vehicle, showcasing real-time data and connectivity features.

In Kürze

  • Telematik steht für die Integration von Telekommunikation und Informatik in Fahrzeugen und revolutioniert die Autolandschaft.
  • Acsia ist ein führender Akteur im Bereich der Telematik-Innovation, der intelligentere, sicherere und effizientere Fahrzeuge für Verbraucher und Flotten entwickelt.
  • Kerntechnologien für die Telematik, flexible Plattformen und innovative Schwerpunkte wie Multicore-Prozessoren und modellbasierte Entwicklung untermauern unsere Kompetenz.
  • Erfahren Sie, wie Telematik Echtzeit-Diagnosen, Routenplanung, Einblicke in das Fahrerverhalten, vorausschauende Wartung und verbesserte Analysen der Fahrzeugnutzung ermöglicht.

Stellen Sie sich eine Welt vor, in der Ihr Fahrzeug nicht nur eine Maschine ist, sondern ein intelligenter Partner. Das ist die Macht der Telematik. Durch die nahtlose Kombination von Kommunikationstechnologien mit bordeigenen Sensoren und Rechenleistung verwandelt die Telematik Fahrzeuge in datenreiche Drehscheiben, die die Art und Weise, wie wir unsere Autos und Lastwagen fahren, warten und mit ihnen interagieren, neu gestalten. Wir bei Acsia kennen das volle Potenzial des vernetzten Fahrzeugs und haben uns verpflichtet, Telematiklösungen zu liefern, die echte Vorteile bringen.

Entschlüsselung der Grundlagen: Die wichtigsten Telematik-Technologien

Stellen Sie sich Telematik als ein System mit mehreren miteinander verbundenen Schichten vor:

  • Die Sensoren: Sie sind die Augen und Ohren Ihres Fahrzeugs. Sie sammeln Daten über alles, von der Motorleistung und dem Kraftstoffverbrauch bis hin zum Reifendruck und dem Fahrerverhalten.
  • GPS & Standortverfolgung: Globale Navigationssatellitensysteme (GNSS) bestimmen die Position Ihres Fahrzeugs, ein Eckpfeiler der Navigation und des Flottenmanagements.
  • Zellulare Konnektivität: Technologien wie 5G NAD und LTE bilden das Kommunikations-Backbone und sorgen für einen konstanten Datenfluss zwischen dem Fahrzeug und der Welt.
  • Fahrzeug-zu-X (V2X): V2X-Protokolle ermöglichen es Ihrem Auto, mit anderen Fahrzeugen und der Infrastruktur zu kommunizieren, was die Kollisionsvermeidung und das Verkehrsmanagement verbessert.

Die Bedeutung von Plattformen

Ein Telematiksystem braucht eine robuste Plattform, um effektiv zu funktionieren:

  • System auf einem Chip (SoC): SoCs integrieren wichtige Komponenten in einem einzigen leistungsstarken Chip und rationalisieren so die Abläufe innerhalb der Telematikeinheit des Fahrzeugs.
  • Betriebssysteme: Die Wahl des Betriebssystems – Linux, Android oder andere – entscheidet über die Softwarekompatibilität und die Leichtigkeit der Anwendungsentwicklung.
  • Klassisches AUTOSAR vs. Moderne Ansätze: Acsia verfügt über fundierte Kenntnisse sowohl im klassischen AUTOSAR, einem Industriestandard, als auch in Open-Source-Plattformen wie POSIX und Linux. So können wir maßgeschneiderte Lösungen entwickeln, die den Bedürfnissen unserer Kunden am besten entsprechen.

Acsia’s Schwerpunktbereiche: Telematik vorantreiben

Wir sind dort innovativ, wo es am wichtigsten ist, um sicherzustellen, dass unsere Lösungen der Zeit immer einen Schritt voraus sind:

  • Multicore-Prozessoren: Die Zunahme datenintensiver Anwendungen erfordert Prozessoren, die in der Lage sind, komplexe Analysen durchzuführen und dabei Entscheidungen in Sekundenbruchteilen zu treffen.
  • Modellgestützte Entwicklung: Wir verwenden ausgefeilte Modellierungstechniken, um Telematiksoftware zu entwerfen, was eine schnellere Entwicklung, höhere Qualität und einfache Aktualisierungen gewährleistet.
  • Wiederverwendbare Frameworks: Vorgefertigte Code-Bibliotheken reduzieren die Entwicklungszeit für kundenspezifische Telematikanwendungen drastisch.
  • Cloud-Container-Lösungen: Die Cloud ergänzt die bordeigenen Systeme und bietet Skalierbarkeit, Flexibilität und Kosteneffizienz bei Bedarf.

Konkrete Vorteile der Telematik: Von Fahrern zu Flotten

Für einzelne Fahrer und Flottenmanager bietet die Telematik eine Fülle von Vorteilen:

  • Echtzeit-Diagnose: Erhalten Sie sofortige Warnungen über mögliche Fehlfunktionen, so dass Sie Probleme beheben können, bevor sie zu großen Problemen werden.
  • Optimierte Routenplanung und Navigation: Intelligente Routenplanung berücksichtigt den Verkehr, Bauarbeiten und sogar die Treibstoffeffizienz und spart so Zeit und Geld.
  • Vorausschauende Wartung: Telematikdaten helfen bei der Vorhersage des Komponentenverschleißes und ermöglichen so eine vorausschauende Wartung, die Pannen verhindert und die Betriebszeit des Fahrzeugs maximiert.
  • Einblicke in das Fahrverhalten: Die Überwachung der Fahrgewohnheiten fördert die Sicherheit und kann Türen zu nutzungsabhängigen Versicherungsrabatten öffnen.
  • Kraftpaket für das Fuhrparkmanagement: Telematik ist für Fuhrparkbetreiber von entscheidender Bedeutung, da sie einen detaillierten Einblick in den Zustand der Fahrzeuge, den Kraftstoffverbrauch, die Leistung der Fahrer und die Routeneffizienz zur Kostenoptimierung bietet.

Acsia: Ihr Partner auf dem Weg zur Telematik

Bei Acsia geht es bei der Telematik nicht nur um Technologie – es geht darum, sinnvolle Auswirkungen auf die Straße zu schaffen. Ob es darum geht, die Sicherheit der Fahrer zu erhöhen, die Betriebskosten zu senken oder neue Geschäftsmodelle zu erschließen – unsere Telematiklösungen sind darauf ausgelegt, einen messbaren Mehrwert zu liefern.

Da sich Fahrzeuge immer mehr zu intelligenten, vernetzten Systemen entwickeln, wird die Rolle der Telematik nur noch wichtiger werden. Mit unserem fundierten Fachwissen, unseren skalierbaren Plattformen und unserer innovationsorientierten Denkweise halten wir nicht nur mit dieser Entwicklung Schritt – wir führen sie an.

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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.