AUTOSAR Expertise

Development and Integration of Advanced AUTOSAR Solutions for Adaptive Cruise Control

  • SW development and validation of on-board system for Adaptive Cruise Control (ACC) feature
  • Elektrobit ACG stack
  • System testing and validation
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Porting of Adaptive AUTOSAR components into Android Automotive for a Global OEM

  • Integration of Adaptive AUTOSAR stack into Android Automotive Vehicle HAL layer
  • Integration of Adaptive AUTOSAR and SOME/IP-based inter-ECU communication
  • Completed in 35 days!
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AUTOSAR Expertise

Development      |      Migration      |      System Health Management      |      Test & Validation

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Boosting Efficiency in Next-Gen ECU Development.

The automotive landscape is experiencing a transformative shift. Consumers are demanding increasingly personalized and feature-rich driving experiences without compromising on safety. The rise of advanced driver-assistance systems (ADAS) and technologies like V2X communication and over-the-air (OTA) software updates necessitates a significant leap forward in ECU software capabilities.
This evolution presents an opportunity for innovation through strategic ECU consolidation. Manufacturers can unlock new possibilities by reducing the number of individual controllers and integrating System-on-Chip (SoC) components. Established platforms like Classic AUTOSAR and Adaptive AUTOSAR play a crucial role in this transformation.

How Acsia Can Help?

Acsia’s capabilities span the complete spectrum of AUTOSAR, addressing the various requirements of ECUs.

Knowledge across consulting, design, and development with various AUTOSAR stacks and micro-controllers.

  • Consulting
    • Impact Assessment
    • Adoption Framework
  • Platform Development: Leverage wide experience in multi-core Classic AUTOSAR platforms and automotive micro-controllers for efficient development.
    • MCAL/CDD Development
    • Standard Development
    • Standardization of Existing CDDs for AUTOSAR
    • Porting, Migration, Configuration, and Support
    • Classic AUTOSAR Stack Vendors: Vector Informatik – DaVinci Configurator Pro/DaVinci Developer, Elektrobit – Tresos Studio, AVIN Systems, Dassault Systèmes – AUTOSAR Builder
    • Micro-controllers: Renesas, Infineon, NXP, ST, Cypress etc.
  • Application Development: Portable application component development.
    • Custom SWC development
    • Model-Based Design & Development

Expertise in establishing continuous integration pipelines, test automation infrastructure, undertaking software to vehicle level testing, and SIL/MIL/HIL simulation and testing.

  • Integration
    • OEM SWC (Software Component) + BSW (Basic Software) + MCAL (Micro-Controller Abstraction Layer)
    • Continuous Integration
    • Automated Integration Testing
  • Verification & Validation
    • Acceptance Testing Framework
    • Test Automation
    • Specific Testing (OEM/Tier-I)
  • In-house AUTOSAR performance tracers & visualizers
    • Extensible and configurable System Health Tracing & Profiling Suite capable of
      • Boot and Application Tracing
      • CPU Profiling
      • Memory Profiling
      • Stack Profiling
      • NVM / Flash Dump Analysis

Acsia’s AUTOSAR capabilities are designed to support OEMs to migrate to standardized architectures and enable ECU consolidation. Comprehensive MCAL development solutions from Acsia, pave the way for the next generation of automobiles.

Project Highlights

Takeover of entire IOC SW responsibility, software maintenance and software integration of all the product lines.

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An end-to-end solution ensuring AUTOSAR integration, FuSa compliance, and ASPICE standards, covering design, validation, diagnostics, and automated HIL testing.

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Development of reusable Cockpit platform in AUTOSAR Version 4.3.

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Software development and validation of an on-board system for Adaptive Cruise Control feature.

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Design, development and testing of Cybersecurity base and application SW components for a smart gateway system with CAN message authentication.
Interacting with the classic AUTOSAR stack vendor, made updates to it.

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Development of rear sear entertainment system in AUTOSAR Version 4.2.2. Acsia Enhanced system security, innovative software architecture, streamlined integration and development processes, compliance with automotive standards.

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Hear From Experts

NIBIL P M
AVP & Head, Advanced Technology Group
With Acsia since 2015
“The shift to software-defined vehicles demands robust, scalable architectures. Acsia’s deep expertise in Classic and Adaptive AUTOSAR, ECU software, and platform integration helps OEMs and Tier-I suppliers accelerate development, ensure compliance, and future-proof mobility solutions. We bring proven experience across the AUTOSAR stack to meet the evolving demands of next-gen vehicles.”
OEM Production Program Experience:
Tier-I Experience:
Why Acsia?
10 years with AUTOSAR
A decade-old relationship with the AUTOSAR consortium as an active member.
Extensive knowledge of AUTOSAR platforms
MCAL/CDD development, multi-core deployments, porting, migration, configuration, integration, testing, and support.
Rich experience working with Classic AUTOSAR stack vendors
Vector Informatik (DaVinci Configurator Pro/DaVinci Developer), Elektrobit (tresos Studio), AVIN Systems, Dassault Systèmes (AUTOSAR Builder).
Proven expertise with leading automotive micro-controllers
Renesas, Infineon, NXP, ST, and Cypress.
Model-Based Development (MBD) Expertise
MBD of system architecture in MATLAB Simulink and StateFlow; MBD for Classic AUTOSAR compliant software architecture; Code generation using Embedded Coder & TargetLink; MISRA compliant code generation.
A decade-old relationship with the AUTOSAR consortium as an active member.
MCAL/CDD development, multi-core deployments, porting, migration, configuration, integration, testing, and support.
Vector Informatik (DaVinci Configurator Pro/DaVinci Developer), Elektrobit (tresos Studio), AVIN Systems, Dassault Systèmes (AUTOSAR Builder).
Renesas, Infineon, NXP, ST, and Cypress.
MBD of system architecture in MATLAB Simulink and StateFlow; MBD for Classic AUTOSAR compliant software architecture; Code generation using Embedded Coder & TargetLink; MISRA compliant code generation.
What’s In It For You

OEMs can focus on developing application-specific functionalities rather than reinventing the wheel for basic software functions, thanks to AUTOSAR’s modular pre-defined software components and standardized interfaces.

OEMs can select and configure AUTOSAR’s standard set of basic software modules (BSW) based on their specific ECU needs, ensuring efficient resource utilization.

AUTOSAR promotes Model-Based Development (MBD) allowing for early error detection, easier configuration, and improved code generation, leading to faster development cycles, better software quality and reliability, and enhanced interoperability between ECUs from different suppliers.

Frequently Asked Questions

AUTOSAR (AUTomotive Open System ARchitecture) is a global development partnership that establishes standardized software architecture for automotive electronic control units. It ensures the scalability, and transferability of software, and addresses safety and availability requirements, facilitating collaboration among various partners in the automotive industry.

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Acsia offers end-to-end AUTOSAR services, including configuration, integration, validation, and Functional Safety compliance for Classic and Adaptive AUTOSAR platforms. The company’s expertise spans system architecture, ECU software development, and testing, ensuring seamless implementation across automotive applications.

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  • Integrated Digital Cockpit for a luxury German OEM through a German Tier-1.
  • A Fail-Safe DC-DC System for an Autonomous Vehicle Platform of a renowned German OEM.
  • Reusable Cockpit Platform for a Tier-1.
  • ADAS ECU for a Finnish Tier-1.
  • Gateway for a North American OEM through a German Tier-1.
  • Rear Seat Entertainment system for a German OEM through a Japanese Tier-1.
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Acsia’s proficiency in both Classic and Adaptive AUTOSAR platforms enables them to assist manufacturers in consolidating ECUs by integrating System-on-Chip components. This approach reduces the number of individual controllers, paving the way for innovative, feature-rich, and safe driving experiences in next-generation automotive systems.

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Yes, Acsia offers software development and validation services for on-board systems implementing features such as Adaptive Cruise Control, by utilizing Classic AUTOSAR stack vendors and providing comprehensive system testing and validation to ensure reliable and safe operation.

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Acsia specializes in integrating Adaptive AUTOSAR stacks into the Android Automotive OS Vehicle Hardware Abstraction Layer (HAL). The company’s expertise in SOME/IP-based inter-ECU communication enhances vehicle software modularity, ensuring smooth interaction between infotainment, safety, and connected services.

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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.
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