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Jenseits von Funktionen: Absicherung Ihres digitalen Cockpits mit funktionaler Sicherheit (FuSa)
by Nibil P M
Driver's hand on a steering wheel with digital cockpit display, representing Acsia commitment to Functional Safety in automotive systems.
Ensuring safety in digital cockpits through Functional Safety (FuSa) principles by Acsia.

In Kürze

  • Funktionale Sicherheit (FuSa), geregelt durch ISO 26262, ist ein systematischer technischer Ansatz zur Gewährleistung der Sicherheit von Automobilsystemen, der insbesondere für das komplexe softwaregesteuerte digitale Cockpit entscheidend ist.
  • Acsia nutzt sein umfassendes Fachwissen im Bereich FuSa, um Automobilhersteller bei der Entwicklung robuster Cockpits zu unterstützen, die die mit Systemfehlern verbundenen Risiken mindern und die Sicherheit von Fahrern und Passagieren gewährleisten.
  • Dieser Artikel befasst sich mit den technischen Feinheiten von FuSa und konzentriert sich dabei auf die Gefahrenanalyse, die Risikobewertung, die Entwicklung von Sicherheitskonzepten sowie die Verifizierungs- und Validierungsprozesse im Zusammenhang mit dem digitalen Cockpit.

Die zunehmende Verbreitung von Software in modernen Fahrzeugen, insbesondere im digitalen Cockpit, hat das Fahrerlebnis revolutioniert. Diese erhöhte Komplexität vergrößert jedoch auch das Potenzial für gefährliche Fehlfunktionen. Funktionale Sicherheit (FuSa), ein systematischer technischer Ansatz, der durch die Norm ISO 26262 geregelt wird, ist der Eckpfeiler, um sicherzustellen, dass diese Systeme zuverlässig und sicher funktionieren, selbst bei Fehlern.

FuSa im digitalen Cockpit verstehen

Bei FuSa geht es im Wesentlichen um Risikomanagement. Es zielt darauf ab, Risiken zu identifizieren, zu bewerten und zu mindern, die sich aus potenziellen Fehlern in elektronischen und elektrischen Systemen ergeben. Im Zusammenhang mit dem digitalen Cockpit bedeutet dies, dass verschiedene Softwarekomponenten, ihre Interaktionen und ihre potenziellen Auswirkungen auf die Fahrzeugsicherheit untersucht werden.

Betrachten Sie die folgenden Beispiele für sicherheitskritische Funktionen innerhalb des digitalen Cockpits:

  • Kombiinstrument: Zeigt wichtige Fahrzeuginformationen wie Geschwindigkeit, Warnungen und Fahrzeugstatus an. Fehlfunktionen können zu Fehlinterpretationen, falschen Fahreraktionen oder sogar zu Unfällen führen.
  • Erweiterte Fahrerassistenzsysteme (ADAS): Funktionen wie der Spurhalteassistent, der adaptive Tempomat und die Notbremsung sind auf genaue Sensordaten und eine zuverlässige Softwareverarbeitung angewiesen. Fehler in diesen Systemen können die Sicherheit des Fahrers gefährden.
  • Navigationssysteme: Falsche oder verzögerte Navigationsinformationen können den Fahrer in gefährliche Situationen führen.

ISO 26262: Das Rahmenwerk für funktionale Sicherheit

ISO 26262 ist eine umfassende Norm, die einen strukturierten Ansatz für FuSa über den gesamten Lebenszyklus von E/E-Systemen im Automobilbereich bietet. Sie umreißt einen V-Modell-Entwicklungsprozess, der Folgendes umfasst:

  1. Konzeptphase: Definition des Umfangs des Objekts, Identifizierung potenzieller Gefahren durch die Gefahrenanalyse und Risikobewertung (HARA) und Festlegung von Automotive Safety Integrity Levels (ASILs) für jedes gefährliche Ereignis auf der Grundlage von Schweregrad, Exposition und Kontrollierbarkeit.
  2. System-Ebene: Entwicklung eines funktionalen Sicherheitskonzepts, das die Sicherheitsziele und technischen Sicherheitsanforderungen für das System definiert. Dazu gehört die Festlegung von Sicherheitsmechanismen wie Redundanz, Diagnose und Fehlertoleranz.
  3. Hardware- und Software-Ebene: Umsetzung der Sicherheitsanforderungen in spezifische Design- und Implementierungsdetails für Hardware- und Softwarekomponenten. Dabei werden Faktoren wie Fehlermodi, Auswirkungen und Diagnoseumfang berücksichtigt.
  4. Integration und Testen: Verifizierung und Validierung, dass das implementierte System die definierten Sicherheitsziele und technischen Sicherheitsanforderungen erfüllt. Dazu gehört eine Kombination aus Simulation, Hardware-in-the-Loop (HIL)-Tests und realen Fahrzeugtests.

Sicherheit im digitalen Cockpit

Ein FuSa-konformes digitales Cockpit wird nach dem Prinzip der Sicherheit entwickelt. Dazu gehören mehrere Schlüsselstrategien:

  • Redundanz: Kritische Funktionen werden durch redundante Systeme unterstützt, die sicherstellen, dass das Fahrzeug auch bei Ausfall eines Primärsystems sicher weiterfahren kann.
  • Diversität: Für redundante Systeme werden unterschiedliche Technologien oder Implementierungsansätze verwendet, um das Risiko von Ausfällen mit gleicher Ursache zu minimieren.
  • Überwachung und Diagnostik: Das Cockpit überwacht ständig seinen eigenen Zustand, erkennt und isoliert Fehler und leitet entsprechende Sicherheitsmaßnahmen ein.
  • Ausfallsichere Systeme: In einigen Fällen kann das System so konzipiert sein, dass es auch bei einem Fehler mit eingeschränkter Funktionalität weiterläuft und einen sicheren Zustand bietet, bis Reparaturen durchgeführt werden können.


Acsia: Ihr Partner für funktionale Sicherheit

Acsia weiß um die entscheidende Bedeutung von FuSa in digitalen Cockpits. Wir nutzen unsere umfassende Erfahrung in der Automobilsicherheit, um Automobilhersteller in jeder Phase der FuSa-Implementierung zu unterstützen:

  • Gefährdungsanalyse und Risikobewertung (HARA): Wir führen eine gründliche HARA durch, um potenzielle Gefahren zu identifizieren und zu bewerten und ein umfassendes Verständnis der Sicherheitsrisiken zu gewährleisten.
  • Entwicklung von Sicherheitskonzepten: Wir arbeiten mit Ihnen zusammen, um Sicherheitsziele zu definieren und robuste Sicherheitskonzepte zu entwickeln, die die Anforderungen der ISO 26262 erfüllen oder übertreffen.
  • Verifizierung und Validierung: Wir bieten umfassende Test- und Validierungsdienste, um sicherzustellen, dass Ihr digitales Cockpitsystem den Normen für funktionale Sicherheit entspricht und unter allen Bedingungen zuverlässig funktioniert.

Durch die Einbindung der Funktionalen Sicherheit in jede Entwicklungsphase können Automobilhersteller digitale Cockpits entwickeln, die Innovation und Zuverlässigkeit in Einklang bringen. Sie bieten fortschrittliche Benutzererfahrungen und gewährleisten gleichzeitig höchste Sicherheitsstandards für Fahrer und Passagiere – mit Acsia als vertrauenswürdigem Partner auf diesem Weg.

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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.