Success Story

Optimizing Digital Cockpit Performance and Architecture with HPCC for a Leading OEM

Discover how Acsia’s innovative solutions and technical expertise enabled a leading Tier-I supplier to overcome critical challenges in optimizing the performance and architecture of their digital cockpit solution, ensuring they met their production deadlines.

Business & Technology Landscape

In October 2023, the Indian automotive industry was at the forefront of integrating high-performance computing clusters (HPCC) into vehicle systems. This advancement significantly enhanced the capabilities of advanced driver-assistance systems (ADAS) and autonomous driving technologies. The use of HPCC in electric and autonomous vehicles enabled real-time data processing, which improved safety and overall vehicle performance.

Modern HPCC systems allowed for the consolidation of multiple electronic control units (ECUs) into a single high-performance unit. This integration supported advanced functionalities such as real-time navigation, vehicle-to-everything (V2X) communication, and sophisticated driver monitoring systems. Additionally, HPCC-powered digital cockpits merged infotainment, instrument clusters, and head-up displays into a unified, user-friendly interface, enhancing the driving experience by providing seamless connectivity and intuitive features.

Collaborative efforts by industry leaders, including Visteon and Qualcomm, focused on developing next-generation digital cockpits and HPCC solutions. These efforts aimed to deliver advanced vehicle features more efficiently, reflecting a broader trend towards more sophisticated, secure, and user-centric automotive technologies. India’s alignment with these global innovations underscored its commitment to advancing automotive technology and improving vehicle safety and performance.

Customer Problem Statement

A leading Tier-I automotive supplier faced critical challenges in aligning their digital cockpit solution with the performance and stability requirements of their OEM partner. The system, based on a Qualcomm chipset with a QNX cluster and Android infotainment running on a QNX virtual machine, was not meeting performance KPIs. With the vehicle launch deadline looming in, the client required a reliable partner to review the entire program’s architecture, identify gaps, and support in fixing those issues.

Acsia Solution

Acsia was engaged to analyse the system and software architecture and provide solutions for the most critical software issues identified by the client. Our scope included:

System Software Engineering: Reviewing and enhancing the base system architecture.

Performance Engineering: Addressing performance bottlenecks and improving system stability.

Video, Graphics, and Display: Enhancing the video and graphics performance for a smoother user experience.

Android Framework and System Networking: Optimizing the Android framework and network performance.

A dedicated team of 15+ engineers from Acsia supported the OEM alongside the Tier-I supplier to implement these improvements.

Business Outcome & Impact

Thanks to Acsia’s intervention, the client was able to resolve the critical performance and stability issues in time, meeting the OEM’s production deadlines. The successful collaboration ensured that the vehicle launch proceeded as planned.

Key Learning

This project underscored Acsia’s ability to handle complex infotainment projects and hypervisor-based architectures. Our expertise in end-to-end system architecture, performance engineering, and innovative problem-solving proved crucial in delivering results under tight deadlines.

Expert Speak

Gururaj Gopal Kulkarni
SME
Our primary objective was to thoroughly analyse the existing system and software architecture to identify the root causes of the performance and stability issues. By leveraging our deep expertise in system software engineering and performance optimization, we were able to provide targeted solutions that not only addressed the immediate problems but also enhanced the overall robustness of the digital cockpit system.
Gloria Joseph
Delivery Head
The success of this project was a testament to our team's dedication and technical prowess. Working closely with the Tier-I supplier and the OEM, we implemented critical enhancements within a very tight timeframe. Our collaborative approach and ability to quickly adapt to the client's needs were key factors in ensuring that the vehicle launch proceeded as scheduled.
Request a Meeting
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.
This site is registered on wpml.org as a development site. Switch to a production site key to remove this banner.