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Success Story

Optimierung der Leistung und Architektur des digitalen Cockpits mit HPCC für einen führenden OEM

Erfahren Sie, wie Acsia mit seinen innovativen Lösungen und seiner technischen Expertise einen führenden Tier-I-Zulieferer in die Lage versetzte, kritische Herausforderungen bei der Optimierung der Leistung und Architektur seiner digitalen Cockpit-Lösung zu meistern und so die Einhaltung der Produktionsfristen zu gewährleisten.

Business & Technology Landscape

Im Oktober 2023 war die indische Automobilindustrie Vorreiter bei der Integration von High-Performance-Computing-Clustern (HPCC) in Fahrzeugsysteme. Dieser Fortschritt hat die Fähigkeiten von fortschrittlichen Fahrerassistenzsystemen (ADAS) und autonomen Fahrtechnologien erheblich verbessert. Der Einsatz von HPCC in elektrischen und autonomen Fahrzeugen ermöglichte die Verarbeitung von Daten in Echtzeit, was die Sicherheit und die Gesamtleistung des Fahrzeugs verbesserte.

Moderne HPCC-Systeme ermöglichten die Konsolidierung mehrerer elektronischer Steuergeräte (ECUs) in einer einzigen Hochleistungseinheit. Diese Integration unterstützte fortschrittliche Funktionen wie Echtzeit-Navigation, Vehicle-to-Everything (V2X) Kommunikation und hochentwickelte Fahrerüberwachungssysteme. Darüber hinaus verschmolzen HPCC-gestützte digitale Cockpits Infotainment, Kombiinstrumente und Head-up-Displays zu einer einheitlichen, benutzerfreundlichen Schnittstelle, die das Fahrerlebnis durch nahtlose Konnektivität und intuitive Funktionen verbessert.

Die Zusammenarbeit von Branchenführern, darunter Visteon und Qualcomm, konzentrierte sich auf die Entwicklung von digitalen Cockpits und HPCC-Lösungen der nächsten Generation. Diese Bemühungen zielten darauf ab, fortschrittliche Fahrzeugfunktionen effizienter bereitzustellen und spiegeln den allgemeinen Trend zu anspruchsvolleren, sicheren und nutzerorientierten Automobiltechnologien wider. Die Ausrichtung Indiens auf diese globalen Innovationen unterstreicht sein Engagement für die Weiterentwicklung der Automobiltechnologie und die Verbesserung der Sicherheit und Leistung von Fahrzeugen.

Customer Problem Statement

Ein führender Tier-I-Automobilzulieferer stand vor der kritischen Herausforderung, seine digitale Cockpit-Lösung mit den Leistungs- und Stabilitätsanforderungen seines OEM-Partners in Einklang zu bringen. Das System, das auf einem Qualcomm-Chipsatz mit einem QNX-Cluster und Android-Infotainment auf einer virtuellen QNX-Maschine basierte, erfüllte die Leistungs-KPIs nicht. Da der Termin für die Markteinführung des Fahrzeugs immer näher rückte, benötigte der Kunde einen zuverlässigen Partner, der die gesamte Programmarchitektur überprüft, Lücken identifiziert und bei der Behebung dieser Probleme hilft.

Acsia Solution

Acsia wurde beauftragt, die System- und Softwarearchitektur zu analysieren und Lösungen für die kritischsten vom Kunden identifizierten Softwareprobleme zu finden. Unser Aufgabenbereich umfasste:

Entwicklung von Systemsoftware: Überprüfen und Verbessern der grundlegenden Systemarchitektur.

Performance Engineering: Behebung von Leistungsengpässen und Verbesserung der Systemstabilität.

Video, Grafik und Anzeige: Verbessern Sie die Video- und Grafikleistung für ein reibungsloses Benutzererlebnis.

Android-Framework und Systemvernetzung: Optimierung des Android-Frameworks und der Netzwerkleistung.

Ein engagiertes Team von mehr als 15 Ingenieuren von Acsia unterstützte den OEM zusammen mit dem Tier-I-Lieferanten bei der Umsetzung dieser Verbesserungen.

Business Outcome & Impact

Dank des Eingreifens von Acsia konnte der Kunde die kritischen Leistungs- und Stabilitätsprobleme rechtzeitig beheben und die Produktionsfristen des OEM einhalten. Die erfolgreiche Zusammenarbeit stellte sicher, dass der Start des Fahrzeugs wie geplant erfolgen konnte.

Key Learning

Dieses Projekt unterstrich die Fähigkeit von Acsia, komplexe Infotainment-Projekte und Hypervisor-basierte Architekturen zu bewältigen. Unser Fachwissen in den Bereichen End-to-End-Systemarchitektur, Performance-Engineering und innovative Problemlösungen erwies sich als entscheidend für die Erzielung von Ergebnissen unter engen Zeitvorgaben.

Expert Speak

Gururaj Gopal Kulkarni
Gururaj Gopal Kulkarni
KMU
Vector
Unser vorrangiges Ziel war es, die bestehende System- und Softwarearchitektur gründlich zu analysieren, um die Ursachen für die Leistungs- und Stabilitätsprobleme zu ermitteln. Durch den Einsatz unserer fundierten Fachkenntnisse in den Bereichen Systemsoftware-Engineering und Leistungsoptimierung waren wir in der Lage, gezielte Lösungen anzubieten, die nicht nur die unmittelbaren Probleme behoben, sondern auch die allgemeine Robustheit des digitalen Cockpit-Systems verbesserten.
Gloria Joseph
Gloria Joseph
Lieferung Kopf
Vector
Der Erfolg dieses Projekts war ein Beweis für das Engagement und die technischen Fähigkeiten unseres Teams. In enger Zusammenarbeit mit dem Tier-I-Lieferanten und dem OEM haben wir entscheidende Verbesserungen innerhalb eines sehr engen Zeitrahmens umgesetzt. Unser kooperativer Ansatz und unsere Fähigkeit, uns schnell an die Bedürfnisse des Kunden anzupassen, waren Schlüsselfaktoren, um sicherzustellen, dass der Start des Fahrzeugs wie geplant stattfinden konnte.
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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.