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Success Story

Entwicklung und Integration von fortschrittlichen AUTOSAR-Lösungen für die adaptive Geschwindigkeitsregelung

Ein führender Softwareanbieter, der weltweit OEMs beliefert, stand vor der Aufgabe, ein On-Board-System für die adaptive Geschwindigkeitsregelung (ACC) zu entwickeln und zu validieren, und das innerhalb einer dringenden Produktionsfrist. In dieser Fallstudie wird untersucht, wie Acsia sein Fachwissen in klassischem und adaptivem AUTOSAR zusammen mit seiner Kompetenz in modellbasierter Entwicklung und ADAS-Lösungen genutzt hat, um die Erwartungen des Kunden zu erfüllen und zu übertreffen.

Business & Technology Landscape

Im Jahr 2021 hat die Automobilindustrie bedeutende Fortschritte bei AUTOSAR und ADAS gemacht:

AUTOSAR Trends und Innovationen

Adaptive AUTOSAR: Höhere Flexibilität und Skalierbarkeit durch Unterstützung dynamischer Software-Updates, serviceorientierter Architekturen und komplexer Algorithmen, die für das autonome Fahren benötigt werden.

Integration mit klassischem AUTOSAR: Verstärkte Anstrengungen zur Integration von adaptiven und klassischen AUTOSAR-Plattformen, Verbesserung der Ressourcenverwaltung und Kommunikation.

Verbesserungen bei der Cybersicherheit: Einführung von robusten Cybersicherheitsmaßnahmen, einschließlich verbesserter kryptographischer Algorithmen und sicherer Kommunikationsprotokolle zum Schutz vor Cyberbedrohungen.

Standardisierungsbemühungen: Schwerpunkt auf der Standardisierung von Schnittstellen und Kommunikationsprotokollen, um die Interoperabilität zwischen Herstellern und Zulieferern zu fördern, die für die Entwicklung autonomer und vernetzter Fahrzeuge entscheidend ist.

ADAS Trends und Innovationen:

KI und maschinelles Lernen: Verbesserte Funktionen wie adaptiver Tempomat, Spurhalteassistent und Fußgängererkennung durch KI und maschinelle Lernalgorithmen.

Sensor-Fusion: Fortschritte in der Sensorfusionstechnologie, die Daten von Kameras, Lidar-, Radar- und Ultraschallsensoren kombiniert, verbessern die Präzision von ADAS.

V2X-Kommunikation: Fortschritte in der Vehicle-to-everything (V2X) Kommunikationstechnologie, die es Fahrzeugen ermöglicht, miteinander und mit der Infrastruktur zu kommunizieren, was das Situationsbewusstsein verbessert und kooperatives Fahren unterstützt.

Einhaltung gesetzlicher Vorschriften: ADAS-Entwicklungen werden von den sich weiterentwickelnden Sicherheitsvorschriften und -standards beeinflusst, die für mehr Sicherheit und Zuverlässigkeit sorgen.

Customer Problem Statement

Der Softwareanbieter für globale OEMs benötigte einen Partner für die Softwareentwicklung und Validierung eines On-Board-Systems für die adaptive Geschwindigkeitsregelung (ACC). Angesichts der knappen Produktionsfrist benötigte das Unternehmen einen erfahrenen Partner, der sich sowohl mit klassischem als auch mit adaptivem AUTOSAR auskennt, um eine pünktliche Lieferung und eine hochwertige Integration zu gewährleisten.

Acsia Solution

Acsia wurde ausgewählt, weil das Unternehmen über große Erfahrung mit klassischem und adaptivem AUTOSAR verfügt, was für dieses Projekt entscheidend war. Das Team behandelte:

Komplette Softwareentwicklung und Integration einer klassischen AUTOSAR-basierten Sicherheitsplattform, die eine serviceorientierte Architektur mit SOME/IP-Diensten über Ethernet nutzt.

Entwicklung kritischer Softwarekomponenten unter Verwendung des Elektrobit ACG Stacks und modellbasiertes Design für Funktionen wie Trajektorie und Lokalisierung.

Software-Entwicklung und Integration einer adaptiven AUTOSAR-basierten Hochleistungsplattform, einschließlich der Einrichtung des NVIDIA Jetson-Boards, Integration von Drittanbieter-Kameras, GPS, IMU-Einheiten und Implementierung eines Trajektorienplaners auf der Grundlage von Objekterkennungs- und Sensorfusionsalgorithmen.

Systemtests und -validierung, wobei die Realisierung der adaptiven Geschwindigkeitsregelung in einem realen Fahrzeug auf der Grundlage offener Standards ein besonderer Höhepunkt war.

Business Outcome & Impact

Der Kunde konnte seine knappe Produktionsfrist erfolgreich einhalten. Die Partnerschaft mit Acsia ermöglichte es ihm, fortschrittliche AUTOSAR- und ADAS-Technologien zu nutzen und eine qualitativ hochwertige und pünktliche Lieferung zu gewährleisten.

Key Learning

Fachkenntnisse in der modellbasierten Entwicklung für den ADAS-Bereich.

Fortgeschrittene Kenntnisse in der Entwicklung von ADAS Active Cruise Control.

Beherrschung des Einsatzes von sowohl adaptiven als auch klassischen AUTOSAR-Systemen.

Expert Speak

Nibil P M
Nibil P M
AVP Erweiterte Technologie-Gruppe
Vector
Die Integration der fortschrittlichen Funktionen sowohl der klassischen als auch der adaptiven AUTOSAR-Systeme erforderte ein gründliches Verständnis der Technologien und eine sorgfältige Planung. Die Fähigkeit unseres Teams, komplexe Systeme wie den adaptiven Tempomat zu entwickeln und zu validieren und gleichzeitig eine nahtlose Integration zu gewährleisten, war ein Schlüsselfaktor für den Erfolg des Projekts.
Anil Shahul Hameed
Anil S
VP Lieferung
Vector
Acsias Engagement, qualitativ hochwertige Lösungen innerhalb enger Fristen zu liefern, war bei diesem Projekt offensichtlich. Die fundierte Expertise unseres Teams in klassischem und adaptivem AUTOSAR, gepaart mit unserer Erfahrung in modellbasierter Entwicklung, ermöglichte es uns, die Anforderungen des Kunden zu erfüllen und seine Erwartungen zu übertreffen.
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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.