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Funktionale Sicherheit in Kfz-Systemen navigieren: Wie Acsia den Weg vorgibt
Advanced automotive dashboard with digital displays and controls, highlighting Acsia’s expertise in ensuring functional safety and ISO 26262 compliance.
Advanced automotive dashboard showcasing digital displays and controls, illustrating Acsia’s commitment to functional safety in modern vehicles.

In Kürze

  • Funktionale Sicherheit ist in modernen Fahrzeugen, die immer komplexer werden, von entscheidender Bedeutung.
  • Die Norm ISO 26262 dient als weltweiter Maßstab für funktionale Sicherheit in der Automobilindustrie.
  • Acsia bietet ein umfassendes Angebot an Dienstleistungen im Bereich der funktionalen Sicherheit, die Unternehmen der Automobilindustrie dabei helfen, die Zuverlässigkeit und Sicherheit ihrer Systeme zu gewährleisten.
  • Das umfassende Fachwissen unseres Teams, die maßgeschneiderten Lösungen und unser Engagement für die Zusammenarbeit machen uns zu einem führenden Unternehmen im Bereich der funktionalen Sicherheit in der Automobilindustrie.

Die Welt des Automobils befindet sich in einem nie dagewesenen Wandel. Die Grenzen zwischen Fahrzeugen und hochentwickelten Computern verschwimmen von Tag zu Tag mehr. Fortschrittliche Fahrerassistenzsysteme (ADAS), nahtlose Konnektivität, das Versprechen des autonomen Fahrens und die immer stärkere Abhängigkeit von Software verändern die Art und Weise, wie wir Autos erleben, grundlegend. Diese Fortschritte eröffnen ein unglaubliches Potenzial, aber sie erfordern auch ein verstärktes Augenmerk auf die Sicherheit – und genau hier kommt die funktionale Sicherheit ins Spiel.

Was ist funktionale Sicherheit, und warum ist sie wichtig?

Im Zusammenhang mit der Automobilindustrie ist die funktionale Sicherheit die Disziplin, die sicherstellt, dass das komplexe Netzwerk elektrischer und elektronischer Systeme in Fahrzeugen einwandfrei funktioniert. Sie schützt vor unangemessenen Risiken, die sich aus Fehlfunktionen oder unerwartetem Systemverhalten ergeben. Je mehr Software im Auto steckt, desto größer ist das Potenzial für Fehler oder unvorhergesehene Probleme. Die funktionale Sicherheit bietet einen systematischen Weg, diese Risiken zu identifizieren, zu analysieren und zu beseitigen, um letztlich Fahrer, Passagiere und alle anderen Verkehrsteilnehmer zu schützen.

Die weltweite Automobilindustrie orientiert sich bei der funktionalen Sicherheit an der Norm ISO 26262. Diese Norm umreißt einen risikobasierten Ansatz für die Entwicklung sicherer Automobilsysteme, der jede Phase des Produktlebenszyklus akribisch berücksichtigt – vom ersten Konzept und Design über die Produktion und den laufenden Betrieb bis hin zur Stilllegung.

Acsia: Ihr Partner für funktionale Sicherheit im Automobilbereich

Wir bei Acsia verstehen die Komplexität und die hohen Anforderungen, die mit der funktionalen Sicherheit in der Automobilindustrie verbunden sind. Unser Team aus erfahrenen Experten verfügt über einen reichen Erfahrungsschatz und ein tiefes Verständnis der ISO 26262. Wir sind in der Lage, ein umfassendes Spektrum an Dienstleistungen anzubieten, die sicherstellen, dass Ihre Systeme die höchsten Sicherheitsstandards erfüllen und übertreffen:

  • Sicherheitsmanagement: Wir helfen Ihnen dabei, eine Sicherheitskultur in Ihrem Unternehmen einzuführen. Dazu gehören die Einrichtung robuster Sicherheitsprozesse, die Implementierung eines maßgeschneiderten Qualitätsmanagementsystems (QMS), die Entwicklung umfassender Sicherheitspläne und die sorgfältige Erstellung aller erforderlichen ISO 26262-Dokumente.
  • Systementwicklung: Unser technisches Fachwissen kommt Ihnen bei der Gestaltung und Entwicklung Ihres Systems zugute. Wir führen eine gründliche Gefahren- und Risikoanalyse (HARA) sowie eine Fehlermöglichkeits- und -einflussanalyse (FMEA) durch und unterstützen Sie bei der Erstellung robuster technischer Sicherheitskonzepte, die der Sicherheit von Anfang an Priorität einräumen.
  • Software-Entwicklung: Wir stellen sicher, dass Ihre Automobilsoftware den strengsten Sicherheitsstandards entspricht. Unsere Dienstleistungen umfassen Code-Reviews, strenge Softwaretests, Bewertungen der Cybersicherheit und Unterstützung bei der Anpassung Ihrer Entwicklungsprozesse an ISO 26262 und Automotive SPICE.
  • Unterstützende Prozesse: Wir glauben, dass funktionale Sicherheit über Design und Entwicklung hinausgeht. Wir arbeiten mit Ihnen zusammen, um die Prozesse im Zusammenhang mit dem Konfigurationsmanagement, dem Änderungsmanagement, der Toolqualifizierung und anderen kritischen Bereichen, die sich direkt auf die Einhaltung von Vorschriften und die Systemintegrität auswirken, zu optimieren.

Der Acsia-Unterschied: Zusammenarbeit, Kompetenz und Ergebnisse

Wir bei Acsia sehen uns nicht nur als Dienstleister, sondern als echte Partner auf Ihrem Weg zu kompromissloser Fahrzeugsicherheit. Unser kooperativer Ansatz ist darauf ausgerichtet, sich nahtlos in Ihr Team zu integrieren. Wir wissen, dass jedes Automobilunternehmen seine eigenen Bedürfnisse und Herausforderungen hat. Deshalb schneidern wir unsere Lösungen speziell auf Sie zu und sorgen dafür, dass die Sicherheit in die Struktur Ihrer Systeme eingewoben wird. Dieses Engagement für eine partnerschaftliche Zusammenarbeit in Verbindung mit unserem unübertroffenen Fachwissen macht Acsia zu einem der führenden Anbieter im Bereich der funktionalen Sicherheit in der Automobilindustrie.

Machen Sie den nächsten Schritt in Richtung kompromisslose Sicherheit

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.