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Success Story

Portierung von adaptiven AUTOSAR-Komponenten in Android Automotive für einen globalen OEM

Ein globaler OEM begann mit der Entwicklung eines Prototyps für ein Android-basiertes Infotainment-System, das sich über das SOME/IP-Protokoll nahtlos in andere Steuergeräte integrieren lässt. Die Initiative zielte darauf ab, die herkömmliche Android-Kommunikationsschicht durch eine adaptive AUTOSAR-Schicht zu ersetzen und so die Kommunikationsmöglichkeiten im Fahrzeug zu verbessern.

Business & Technology Landscape

Das Projekt begann vor dem Hintergrund der wichtigsten Trends in der Automobilindustrie:

AUTOSAR Adaptive Plattform: Diese Plattform wurde vom AUTOSAR-Konsortium entwickelt, um den steigenden Anforderungen an die Komplexität und Konnektivität von Automobilsystemen gerecht zu werden, die für das autonome Fahren und fortschrittliche Fahrerassistenzsysteme (ADAS) entscheidend sind.

Zentralisierte E/E-Architekturen: Die OEMs gingen zu zentralisierten Architekturen über, um die Systemeffizienz und -flexibilität zu erhöhen. Dabei wurden fortschrittliche Virtualisierungstechnologien und AUTOSAR-Erweiterungen in die Steuergeräte-Hardware integriert.

Android Automotive OS: Wird zunehmend als natives Betriebssystem eingesetzt, das vollständig in die Fahrzeughardware integriert ist. Trotz der Verfügbarkeit von Android 12 wurde Android 11 für dieses Projekt verwendet.

Weitverbreitete Annahme: Große Hersteller, darunter Volvo, Polestar und General Motors, setzen bei ihren neuen Fahrzeugmodellen standardmäßig auf Android Automotive OS.

Customer Problem Statement

Nach der Migration der Funktionen für das autonome Fahren von einem sicheren Linux auf QNX wollte der OEM den Prototyp eines Android-basierten Infotainmentsystems entwickeln, das in der Lage ist, über das SOME/IP-Protokoll direkt mit anderen Steuergeräten zu kommunizieren, die über eine adaptive AUTOSAR-Kommunikationsschicht integriert sind.

Acsia Solution

Acsia, bekannt für seine Expertise bei digitalen Cockpit-Lösungen, wurde mit dem Projekt betraut. Das Acsia-Team übernahm die Integration des Adaptive AUTOSAR-Stacks in die Android Automotive Vehicle HAL-Schicht.

  • Erstellen von Beispiel-Softwarekomponenten (SWCs)
  • Für eine robuste Integration, etablierte Kommunikationswege von HVAC und SWC

Business Outcome & Impact

Rechtzeitige Lösung: Das Projekt wurde in nur 35 Tagen abgeschlossen, weit vor der geplanten Zeitspanne von sechs Monaten.

Rationalisierte Inter-ECU-Kommunikation: Durch die Integration von Adaptive AUTOSAR und SOME/IP-basierter Inter-ECU-Kommunikation hat Acsia die Kommunikation für zukunftsweisende Systeme erfolgreich rationalisiert.

Wiederverwendbarkeit: Die Umstellung auf eine gemeinsame Schnittstellensprache für alle Systeme erhöhte die betriebliche Effizienz und verkürzte die Entwicklungszeiten für zukünftige Projekte.

Key Learning

Dieses Projekt hat gezeigt, dass Acsia in der Lage ist, komplexe Software-Integrationen im Automobilsektor zu steuern. Die erfolgreiche Implementierung von Adaptive AUTOSAR-Komponenten zusammen mit Android Automotive und die Einrichtung eines schlankeren Kommunikationsprozesses ist ein Präzedenzfall für zukünftige Softwareentwicklungen im Automobilbereich.

Expert Speak

Vasanthraj G Pillai
Vasantharaj G
VP Technologie & Innovation
Vector
Die Integration der IP-basierten Inter-ECU-Kommunikation über Adaptive AUTOSAR mit Android Automotive ist ein Sprung in die Zukunft. Diese futuristische Technologie verbessert die Reaktionsfähigkeit und Intelligenz des Fahrzeugs und verspricht ein vernetztes und intuitives Fahrerlebnis für die Nutzer.
Anil Shahul Hameed
Anil S
VP Lieferung
Vector
Die Tatsache, dass unser Team den Prototyp in nur 35 Tagen und damit weit vor Ablauf der Frist liefern konnte, unterstreicht unser Engagement für Spitzenleistungen und Schnelligkeit bei Innovationen. Dieses Projekt hat die Erwartungen des OEMs nicht nur erfüllt, sondern übertroffen und den Weg für zukünftige Kooperationen geebnet.
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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.