Digital Cockpit
and Display

Transforming in-vehicle experiences for drivers and passengers.

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.
Advancements in display technology, processing power, and user interfaces are creating highly customizable and interactive in-vehicle experiences. Current trends highlight the rise of larger, higher-resolution screens, head-up displays, and seamless integration of infotainment, navigation, and driver assistance systems.

How Acsia Can Help?

Robust requirements management, software architecture, and system integration for digital cockpits.

  • Performance Analysis & Optimization: Leverage OS expertise and custom tools to analyze the system. Improve system performance and meet critical KPIs.

CI/CD pipelines for seamless integration for code quality checks and automated testing.

Intuitive user interface (UI) and user experience (UX) development that prioritizes driver safety and minimizes distraction.

  • Kanzi, Android HMI, Qt QML

Ensure secure and reliable data exchange between the digital cockpit and external systems such as telematics, cloud, and V2X.

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

Develop robust security design, implement software requirements and undertake testing to protect connected cockpits from cyber threats.

Ensure adherence to industry standards in automotive software development and testing.

  •  Develop Functional Safety (FuSa) designs and work products adhering to ISO 26262
  • Create packages for digital cockpits adhering to Automotive SPICE® L3.

For display screens (information, entertainment, navigation) and connected car features like ADAS (advanced driver assistance system).

  • Middleware: Media, Audio, Connectivity, Graphics
  • OS & SW Platforms: Linux, Android, QNX, AUTOSAR, Adaptive AUTOSAR
  • RTOS: Zephyr, ThreadX, OSEK
  • HW Platforms: Renesas, Infineon, NXP, Microchip, Cypress, TI, NVIDIA, and Qualcomm

Project Highlights

Development and maintenance of digital Cockpit programs before the corresponding SOP.

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  1. Development of Android-based connected infotainment (RSE).
  2. Responsible for 80% of the development, complete software integration and software testing.
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Identify performance bottlenecks and implement optimization solution for Autonomous Driving functions while porting from Safe Linux to QNX.

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Ownership of all code developed by the original supplier and migrating to Android 13 on customer hardware.

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Multiple cluster production programs through North American Tier-1.

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  1. Design and development of HMI (cluster + infotainment + HMI server).
  2. Integration of Navigation system with system HMI.
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  1. Tier-1 facing challenges in System Performance and System Architecture.
  2. The system – Digital Cockpit based on Qualcomm chipset, QNX Cluster and Android Infotainment running on a QNX Virtual Machine.
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Hear From Experts

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:
Tier-I Experience:
Why Acsia?
Deep Domain Expertise
In-depth understanding of the automotive industry and its specific needs, regulations, and safety standards.
Cutting-Edge Technology Stack
Mastery of relevant technologies like high-performance graphics engines, automotive-grade Android, QNX and Linux, and HMI development tools like KANZI, and QT/QML.
AUTOSAR®, V2X, FuSa, Cybersecurity Expertise
A decade of experience in automotive industry-specific software architecture, platforms, protocols, and best practices.
Seamless Integration with ADAS Systems
Proven ability to integrate software with advanced driver-assistance systems (ADAS) for a comprehensive safety and driver experience package.
User-Centered Design (UCD) Approach
A laser focus on user experience (UX) with a proven UCD methodology to create intuitive, safe, and visually appealing digital cockpits.
Agile Development & Global Delivery
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.
Boutique Mindset
Small enough to be nimble yet scalable to address large project requirements quickly.
In-depth understanding of the automotive industry and its specific needs, regulations, and safety standards.
Mastery of relevant technologies like high-performance graphics engines, automotive-grade Android, QNX and Linux, and HMI development tools like KANZI, and QT/QML.
A decade of experience in automotive industry-specific software architecture, platforms, protocols, and best practices.
Proven ability to integrate software with advanced driver-assistance systems (ADAS) for a comprehensive safety and driver experience package.
A laser focus on user experience (UX) with a proven UCD methodology to create intuitive, safe, and visually appealing digital cockpits.
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.
Small enough to be nimble yet scalable to address large project requirements quickly.
What’s In It For You

Leverage Acsia’s expertise and pre-developed software components to accelerate development cycles and get your innovative digital cockpits to market quicker.

Benefit from Acsia’s agile development processes, and global talent pool leading to cost savings compared to in-house development.

Acsia’s focus on user-centric design and cutting-edge interfaces helps brands create intuitive and personalized digital cockpits, setting them apart from their competitors.

Stay ahead of the curve with Acsia’s commitment to technologies like AUTOSAR and secure V2X communication, ensuring your digital cockpits are scalable and adaptable.

Acsia prioritizes safety-critical software development processes and adheres to industry standards, ensuring reliable, compliant, and secure in-vehicle experiences.

By partnering with Acsia, OEMs and Tier-I suppliers leverage their experience in managing complex software projects, minimizing development risks and delivering high-quality digital cockpits.

As your digital cockpit requirements evolve, Acsia can scale its solutions to meet your growing demands. Their agile development methodologies, global delivery capabilities, and experience with various automotive projects allow them to adapt to your specific requirements and integrate seamlessly with existing systems.

Acsia’s experience in managing complex software development projects translates to a smoother workflow for your team. They can handle tasks like software development, integration and testing, freeing your internal resources to focus on core automotive engineering.

Frequently Asked Questions

Acsia delivers a full suite of software solutions for Digital Cockpit & Display systems – including HMI development, software development, software engineering, vehicle systems engineering, system integration, connectivity solutions, verification & validation and cybersecurity solutions. The focus is on creating immersive, future-ready in-vehicle experiences using the latest innovations in automotive technology.

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Acsia believes that a well-designed interface should feel effortless. That’s why the HMI development puts a strong emphasis on intuitive UI and seamless UX – to help drivers stay focused and safe on the road. The company uses platforms like Kanzi, Android HMI, and Qt QML to craft responsive, user-friendly interfaces that cater to the specific needs of the clients.

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Acsia’s solutions are built to run smoothly across all major operating systems, including Linux, Android, QNX, and AUTOSAR. This versatility allows the company to align with each automaker’s tech stack and deliver consistent performance, no matter the OS.

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Acsia’s approach to integration is both structured and agile. Using robust CI/CD pipelines, the company streamlines code quality checks and automated testing. On the systems side, Acsia handles everything from requirements management to architecture design and automotive verification & validation – ensuring every digital cockpit system is both high-performing and reliable.

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Connectivity powers the future of driving – and Acsia makes sure the cockpit is ready for it. The company’s solutions enable secure data exchange between the vehicle and the outside world, supporting telematics, cloud integration, and V2X communication. This allows features like real-time traffic updates, OTA software updates, and smarter driver assistance systems to function seamlessly.

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  • Takeover and Enhancement of Next-Gen Digital Cockpit for a Global OEM.
  • Building one of the world’s first Android-based Rear Seat Entertainment (RSE) system for a German OEM through a leading Japanese Tier-1.
  • Performance Optimisation for High Performance ADAS ECU for a German OEM.
  • Migration of a containerized Android 11 to Android 13.
  • Digital Instrument Cluster SW development for all the models of a North American OEM through a leading North American Tier-1.
  • Connected Cluster Development for India’s leading Electric Scooter Brand.
  • Optimising Digital Cockpit Performance and Architecture with HPCC for an Indian 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 

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/PS04 | 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/PS03 | AI/ML

Context

In a highly competitive automotive market, consumer purchase decisions are influenced by a mix of vehicle features, price, and brand perception. Automakers invest heavily in design and innovation, but it is often unclear which specific features (e.g., mileage, horsepower, safety, infotainment, connectivity) actually drive sales in different regions and demographics.

 

Pain Point

  • Automakers often rely on intuition, surveys, or fragmented market studies, which may not reflect actual consumer behaviour.
  • Without clear insights, companies risk overinvesting in features that don’t influence buying decisions while underestimating the importance of others.
  • This leads to misaligned product strategies, higher costs, and lost opportunities in competitive segments.

 

Challenge

Develop a data-driven AI solution to quantify the importance of car features in consumer purchasing decisions. The system should analyze:

  • Sales data (model, features, trim levels, price).
  • Customer demographics (age, income, region).
  • Market variations (urban vs rural, luxury vs budget segments).

 

Goal

Identify and rank which features most strongly influence purchasing decisions, enabling automakers to:

  • Focus R&D investments on features consumers truly value.
  • Tailor marketing strategies to highlight high-impact features.
  • Customize offerings by region, demographic, or price segment.

 

Outputs

  • Ranked feature importance list (e.g., mileage, price, infotainment, safety).
  • Feature impact segmentation (importance by region, age group, or price tier).
  • Visualization of trade-offs (e.g., mileage vs horsepower vs price sensitivity).

 

Impact

  • Better product design decisions → aligning cars with what customers actually want.
  • Efficient R&D and marketing spend → reduced waste, higher ROI.
  • Stronger competitive positioning → faster response to shifting consumer trends.
  • Scalable model → applicable across new launches, regions, and evolving customer preferences.
AH2025/PS02 | AI/ML

Context

Electric Vehicle (EV) adoption is accelerating globally, driven by sustainability goals and government incentives. However, charging infrastructure development lags behind, and demand at charging stations is often highly variable, influenced by factors such as time of day, location, and weather. This creates challenges for both EV users (availability, waiting times) and city planners (under/over-utilization of infrastructure).

 

Pain Point

  • Charging stations experience unpredictable surges or idle periods, leading to long wait times or wasted infrastructure.
  • City planners and operators struggle to decide how many charging points to allocate at different locations.
  • Poor demand forecasting results in inefficient investment and reduced adoption of EVs due to unreliable charging availability.

 

Challenge

Develop an AI solution that forecasts charging demand at individual stations. The system should take into account:

  • Historical station usage (transactions per hour/day).
  • Temporal patterns (time of day, weekdays vs weekends, seasonality).
  • Geographic location (urban, suburban, highway).
  • External factors such as weather conditions, holidays, or special events.

 

Goal

Provide accurate time-series demand forecasts (hourly/daily) per charging station, enabling operators and planners to:

  • Allocate charging points efficiently.
  • Reduce wait times for EV users.
  • Optimize investment in EV infrastructure.

 

Outputs

  • Predicted demand curves (number of EVs per time unit, per station).
  • Station-level insights (peak usage windows, underutilized stations).
  • Scenario forecasts (e.g., rainy day vs sunny day, weekday vs weekend).

 

Impact

  • Smarter infrastructure planning → efficient use of budget and resources.
  • Improved EV user experience → reduced charging wait times.
  • Accelerated EV adoption → supporting sustainability and emissions reduction.
  • Scalable solution that can be adapted by municipalities, private charging operators, or energy utilities.
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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