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Digital Cockpit
and Display

Ein neues Erlebnis für Fahrer und Passagiere im Fahrzeug.

Die Automobilindustrie erlebt derzeit eine digitale Revolution, bei der das Fahrerlebnis und die Technologien für vernetzte Autos im Mittelpunkt stehen. Digitale Cockpits mit integrierten Displays und Informationssystemen stehen an der Spitze dieses Wandels.
Fortschritte in der Displaytechnologie, der Verarbeitungsleistung und den Benutzeroberflächen schaffen hochgradig anpassbare und interaktive Erlebnisse im Fahrzeug. Aktuelle Trends sind größere, hochauflösende Bildschirme, Head-up-Displays und die nahtlose Integration von Infotainment-, Navigations- und Fahrerassistenzsystemen.

Wie kann Acsia helfen?

Robustes Anforderungsmanagement, Softwarearchitektur und Systemintegration für digitale Cockpits.

  • Leistungsanalyse & Optimierung: Nutzen Sie das Betriebssystem-Know-how und benutzerdefinierte Tools, um das System zu analysieren. Verbessern Sie die Systemleistung und erfüllen Sie wichtige KPIs.

CI/CD-Pipelines für eine nahtlose Integration für Code-Qualitätsprüfungen und automatisierte Tests.

Projekt-Highlights

Entwicklung und Pflege von digitalen Cockpit-Programmen vor der entsprechenden SOP.

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  1. Entwicklung von Android-basiertem vernetztem Infotainment (RSE).
  2. Verantwortlich für 80% der Entwicklung, die vollständige Softwareintegration und Softwaretests.
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Hören Sie von Experten

Nibil P M

NIBIL P M

AVP & Head, Advanced Technology Group
With Acsia since 2015
“The automotive industry’s shift to Software-Defined Vehicles (SDVs), is accelerating the efforts towards ECU consolidation. Acsia brings proven expertise in automotive IVI, ECU consolidation, digital cluster, UI/UX design & development, and verification & validation. Our strong track record with leading IVI suppliers and active involvement in next-gen IVI head unit and cluster production programs uniquely position us to meet OEM and Tier-I requirements.”
OEM Production Program Experience:
  • Bmw
  • MB
  • Volvo
  • Gm
  • Tata
  • Mahindra
Tier-I Experience:
  • Panasonic
  • Aptiv
  • Garmin
Warum Acsia?
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Deep Domain Expertise
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In-depth understanding of the automotive industry and its specific needs, regulations, and safety standards.
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Cutting-Edge Technology Stack
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Mastery of relevant technologies like high-performance graphics engines, automotive-grade Android, QNX and Linux, and HMI development tools like KANZI, and QT/QML.
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AUTOSAR®, V2X, FuSa, Cybersecurity Expertise
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A decade of experience in automotive industry-specific software architecture, platforms, protocols, and best practices.
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Seamless Integration with ADAS Systems
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Proven ability to integrate software with advanced driver-assistance systems (ADAS) for a comprehensive safety and driver experience package.
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User-Centered Design (UCD) Approach
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A laser focus on user experience (UX) with a proven UCD methodology to create intuitive, safe, and visually appealing digital cockpits.
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Agile Development & Global Delivery
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Utilizing agile methodologies to ensure project flexibility and responsiveness, coupled with a global delivery model for cost-efficiency and access to a wider talent pool.
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Boutique Mindset
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Small enough to be nimble yet scalable to address large project requirements quickly.
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In-depth understanding of the automotive industry and its specific needs, regulations, and safety standards.
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Mastery of relevant technologies like high-performance graphics engines, automotive-grade Android, QNX and Linux, and HMI development tools like KANZI, and QT/QML.
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A decade of experience in automotive industry-specific software architecture, platforms, protocols, and best practices.
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Proven ability to integrate software with advanced driver-assistance systems (ADAS) for a comprehensive safety and driver experience package.
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A laser focus on user experience (UX) with a proven UCD methodology to create intuitive, safe, and visually appealing digital cockpits.
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Utilizing agile methodologies to ensure project flexibility and responsiveness, coupled with a global delivery model for cost-efficiency and access to a wider talent pool.
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Small enough to be nimble yet scalable to address large project requirements quickly.
Was ist für Sie drin?
Häufig gestellte Fragen
Ein Treffen anfordern
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.