Human Machine Interface (HMI) Development Expertise

HMI Development Expertise

End-to-end HMI design, development, and validation using advanced tools, OS platforms, and graphics frameworks to deliver adaptive, user-centric, and safety-compliant in-vehicle experiences.

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Creating intuitive and adaptive user interfaces.

The automotive Human-Machine Interface (HMI) is transforming in-vehicle experiences with personalization and advanced intelligent features. Tools like Kanzi for stunning visuals, Android HMI for platform flexibility, and Qt/QML for rapid prototyping enable modern digital cockpits to integrate voice recognition, intuitive touch interfaces, and personalized user experiences.
Innovations by integrating Edge AI enhance convenience by offering personalized assistance. Adhering to safety standards is crucial in today’s connected world which requires improved safety by reducing distractions and interactions with vehicle systems. New-generation HMI solutions adopt a data-centric approach, with sensors capturing critical driver and navigation data, ensuring seamless integration with automotive systems and V2X scenarios, reshaping the driving experience.

How Acsia Can Help?

Acsia specializes in the development, customization, graphics optimization and verification & validation of Automotive Human Machine Interface (HMI).
  • Robust requirements management, analysis tools, system architecture, and system integration for HMI.
  • Performance Analysis & Optimization: Leverage OS expertise, and hardware acceleration capabilities to improve system performance and meet critical KPIs.
  • Expertise in Services-Oriented Architecture (SOA) and data-driven architecture.
  • Layered architecture definitions for user interface (UI), business logic and service layer.
  • HMI Tools: Kanzi, Android HMI, Qt/QML, Slint, Flutter, Unity.
  • Languages: C/C++, Java, Python.
  • Middleware: OpenGL/GLSL, Vulcan.
  • Hardware Platforms: NXP, Renesas, Qualcomm, Nvidia, Infineon, Telechips.
  • OS Platforms: Android, Linux, QNX, Windows, Web.
  • Business Logic
  • Service Layer
  • Middleware
  • Compliance verification with UX design
  • Business Logic verification
  • Architectural verification at interface level
  • Graphics performance verification
  • Test automation framework and robotic test automation for HMI
  • Compliance verification with UX design
  • Business Logic verification
  • Architectural verification at interface level
  • Graphics performance verification
  • Test automation framework and robotic test automation for HMI
  • HMI requirements analysis (SYS and SWE)
  • Standards compliance
  • Code generation and unit test verification
  • Software test case generation
  • Design and development of Intelligent HMI features by integrating Edge AI
  • Create engaging UI/UX as per OEM requirements
  • Design Tools: Adobe Photoshop, Adobe Illustrator, Blender
Acsia’s work in HMI covers the following areas:

Head Unit and Rear Seat Applications

  • Navigation
  • Media
  • Vehicle Functions
  • In-Car Internet
  • Speech Recognition Applications
  • System UI Customizations
  • Other Entertainment Applications

Instrument Cluster HMI

  • Tell-tales, Meters, Dials, Warnings
  • Information On Demand
  • Adapt Navigation Software
  • Driver Assist Functions Integration
  • Head Unit Integration to Clusters
  • EV Specific Functions
  • Other Cluster Functions

Head Up Display HMI

  • HUD Applications
  • Augmented Displays

Project Highlights

  1. CDC with 3 Displays (Configurable and Customizable as required ).
  2. Central Driver Console (Linux QT Cluster + Android IVI in shared display), Rear Left Display, Rear Right Display.
  3. Android 14 Multi-User Multi-Display Configuration.
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Development of Cluster HMI and integration of Navigation system into system HMI.

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End to end development of the Cluster HMI for different variants.

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Development of Cluster and infotainment HMI for their flagship model.

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Development of Android based IVI HMI.

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Developed an Android-based RSE system with Baidu App-store integration, System UI and startup animation customization, framework enhancements, custom G-board keyboard, and in-vehicle browser.

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Hear From Experts
Vasantharaj G Pillai
VP Technology & Innovation
With Acsia since 2014
“The future of in-vehicle experiences lies in smart, seamless HMIs that elevate driver and passenger interaction. Acsia delivers end-to-end HMI solutions — from UX design and middleware to platform integration and testing. With expertise in Android, Linux, and embedded systems, we empower OEMs to build next-gen digital cockpits that are intuitive, reliable, and future-ready.”
OEM Production Program Experience:
Tier-I Experience:
Why Acsia?
HMI Development Since 2014
Acsia has delivered multiple HMI development projects for leading OEMs.
Experience in Leading UI Tools
Kanzi, Android HMI, Qt QML, Slint, Flutter, and Unity.
Experience across OS Platforms
Android, Linux, QNX, and Web.
Experience in Middleware
OpenGL ES and Vulcan.
High-end Graphics Capability
Complex high-definition and high-performance graphics using HMI development tools.
Personalisation of In-Vehicle Infotainment
AR/VR and A/V streaming.
Acsia has delivered multiple HMI development projects for leading OEMs.
Kanzi, Android HMI, Qt QML, Slint, Flutter, and Unity.
Android, Linux, QNX, and Web.
OpenGL ES and Vulcan.
Complex high-definition and high-performance graphics using HMI development tools.
AR/VR and A/V streaming.
What’s In It For You

OEMs benefit from expertise in the latest HMI technologies, ensuring they stay at the forefront of user interface design, voice recognition, augmented reality, and other advanced features.

By leveraging data-driven insights, OEMs can create highly intuitive, user-centric interfaces that prioritize driver comfort, ease of use, and can adapt to individual preferences, ensuring a more engaging and seamless in-vehicle experiences.

OEMs can drive innovative functionality (voice commands, minimal distraction design, and adaptive cruise controls) while ensuring adherence to stringent automotive safety parameters.

By deploying pre-existing tools, platforms, and reusables, OEMs can significantly reduce development time and ensure faster market entry without compromising quality or user experience.

By engaging an HMI development expert like Acsia, OEMs can scale their HMI solutions across different vehicle models and regions, and accelerate innovation without increasing overheads related to inhouse resources and infrastructure.

Frequently Asked Questions

Acsia specializes in industry-leading automotive HMI development tools such as Kanzi, Android HMI, Qt QML, Slint, Flutter, and Unity. The company’s expertise enables OEMs to achieve stunning visuals, rapid prototyping, and enhanced platform flexibility for modern digital cockpits.

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Acsia provides solutions, like vehicle system engineering, performance optimization, software architecture (SOA and data-driven), custom HMI development using Kanzi, Android HMI, Qt QML, Slint, Flutter, and Unity, along with middleware and OS expertise. The company offers rigorous verification, validation, automated testing (including LiLA for HMI), and engaging OEM-specific UI/UX design, ensuring high performance, safety compliance, and accelerated market readiness.

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Yes. Acsia offers customization capabilities across various operating systems, including Android, Linux, QNX, Windows, and Web platforms, as well as hardware platforms such as NXP, Renesas, Qualcomm, Nvidia, Infineon, and Telechips. The company tailors solutions to meet specific OEM requirements.

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By significantly accelerating the development timelines through Acsia’s extensive use of proven  frameworks, reusable software components, and industry expertise, the company is able to accelerate time-to-market for automotive HMI solutions. This approach enables automotive  OEMs to quickly deploy personalized, high-quality HMI solutions without compromising on user  experience or safety.

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Acsia has expertise in developing innovative digital instrument clusters and advanced Head-Up Displays (HUD), like tell-tales, meters, augmented reality displays, navigation adaptation, EV specific interfaces, driver assistance integrations, and seamless cluster-to-head unit integration, transforming in-vehicle experiences.

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  • Low-Cost CDC (Proof of Concept).
  • Connected Cluster Development for an Indian Electric OEM.
  • Digital Cluster HMI Development for a North American OEM.
  • HMI Development for an Indian 2W OEM.
  • Android HMI Development for an Indian OEM.
  • Rear Seat Entertainment Development for a German OEM.
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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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