Automotive Linux Expertise

Building secure and scalable vehicles, faster and cheaper.

The automotive industry is undergoing a digital revolution, with a focus on driver experience and connected car technologies. Digital cockpits, featuring integrated displays and information systems, are at the forefront of this transformation.
Automotive Linux is a viable alternative for the development of entry-to-mid-level infotainment systems. With the addition of a Safety Island, digital clusters can also be implemented with Linux.

How Acsia Can Help?

Deep expertise in ensuring robust and efficient performance across automotive systems.

  • Linux Distribution: Yocto, Ubuntu, Sabaton
  • HW Platforms: Qualcomm, x86, i.MX series, R-Car series, Nvidia, Telechips Dolphin series, Jacinto-4/6, Intel XScale
  • Bootloaders: U-Boot, Little Kernel
  • Toolchains: GNU, Yocto, LLVM

Extensive know-how to meet specific requirements and optimize performance for diverse platforms and applications.

  • Kernel configuration
  • Kernel modules
  • Customizing SW Stacks

Create user-friendly applications across various Linux-based platforms.

  • Middleware: GStreamer, D-Bus, OpenGL ES, OpenVG
  • Application: C/C++, Rust
  • HMI: QT/QML, GTK, Kanzi

Project Highlights

Development of an Rear Seat Entertainment System in an headless secure Linux platform.

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Defined and developed application components for the digital cockpit, including customer and IVI application development, middleware integration, and virtualization of Linux and Android environments for both cloud and local development platforms.

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Optimized boot time for Linux-based Infotainment

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CDC with 3 displays (Configurable and customizable as required)
Central Driver Console with Linux QT cluster and Android IVI shared display.

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Hear From Experts

Sojan James

Principal Architect
25+ years in automotive software and systems
“As cars get smarter, software takes the wheel — and that’s where Acsia comes in. Sabaton, our Automotive Linux platform built in Rust, delivers secure, high-performance ECUs and TCUs. With deep expertise in BSPs, OTA, and in-vehicle messaging via DDS and SOME/IP, we help OEMs and Tier-1s build scalable, production-ready systems that are ready for the road ahead.”
OEM Production Program Experience:
Tier-I Experience:
Why Acsia?
Deep Understanding of Automotive Linux Architecture
Experienced in building high quality and high performance Linux-based automotive products.
Proven Linux Production Program Experience
Ascia has delivered successful programs on Linux for leading OEMs since 2014.
Deep Domain Expertise
In-depth understanding of the automotive industry and its specific needs, regulations, and safety standards.
Tread fast. Tread boldly. Thread safely
Deep knowhow of integrating Rust in Linux-based infotainment systems.
Experienced in building high quality and high performance Linux-based automotive products.
Ascia has delivered successful programs on Linux for leading OEMs since 2014.
In-depth understanding of the automotive industry and its specific needs, regulations, and safety standards.
Deep knowhow of integrating Rust in Linux-based infotainment systems.
What’s In It For You

Automotive Linux thrives on open-source principles, fostering collaboration between carmakers, tech giants, and a vast developer community. This shared effort fuels rapid innovation and feature creation, accelerating development cycles.

Traditionally, carmakers relied on proprietary software, leading to a fragmented landscape. Automotive Linux provides a common platform, allowing for standardized software across brands. This simplifies development for automakers and ensures a more consistent user experience for drivers.

Automotive Linux allows carmakers to create more user-friendly infotainment systems with smartphone-like interfaces and seamless mobile device integration, enhancing the in-car experience.

The Automotive Linux platform is designed to be scalable, allowing carmakers to easily integrate new features and functionalities as connected vehicle technology and autonomous driving systems evolve.

Frequently Asked Questions

Automotive Linux offers cost efficiency, scalability, and faster innovation through open-source collaboration. It reduces software fragmentation, enhances infotainment experiences, and ensures seamless mobile integration. Its adaptability supports evolving connected vehicle and autonomous driving technologies, making it a future-ready choice for automakers.

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Acsia offers expertise in Automotive Linux, covering Linux platform integration (Yocto, Ubuntu, Qualcomm, Nvidia, etc.), Kernel and BSP customization, and application development using C/C++, Rust, QT/QML, GStreamer, and OpenGL ES. The company optimizes performance, customize software stacks, and build secure, scalable automotive solutions.

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Sabaton is Acsia’s Automotive Linux software platform, designed using the Rust programming language to develop secure and high-performance ECUs (Electronic Control Units) and TCUs (Telematics Control Units). It features a network and data-centric architecture, utilizing industry-standard DDS and SOME/IP for efficient message control and communication.

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  • Built a secure Rear Seat Entertainment system using a headless Linux platform for a German OEM through a leading Japanese Tier-1.
  • Takeover and Enhancement of Next-Gen Digital Cockpit for a Global OEM.
  • Linux Boot Time Optimization.
  • Linux Cluster for Low-Cost CDC (Proof of Concept).
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Introducing Sabaton

Sabaton is Acsia’s Automotive Linux software platform, based on the Rust programming language for developing secure, high-performance ECUs (electronic control units) and TCUs (telematics control units). Sabaton is built on a network and data-centric architecture, with industry-standard DDS and SOME/IP as the primary mechanism of message control.

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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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