Success Story

Android-based RSE for a Global Luxury OEM with Third-Party App Store Integration

Find out how Acsia partnered with the Tier-I to develop an Android-based Rear Seat Entertainment (RSE) system for a leading luxury car maker, targeting the Chinese market with compatibility for Baidu’s third-party app store and the Rest of the World markets.

Business & Technology Landscape

In the autumn of 2016, the automotive industry began to significantly change. Electric vehicles and autonomous driving technologies started becoming mainstream, leading to major advancements in car technology. This period also saw a shift in the in-car experience as leading Original Equipment Manufacturers (OEMs) began to incorporate advanced digital interfaces. This meant moving away from traditional mechanical controls to more sophisticated infotainment systems.

Platforms like Apple CarPlay and Android Auto started making interactions within vehicles more intuitive and connected, reflecting a broader trend where cars were becoming not just a means of transportation but a connected mobile environment integrating various digital aspects.

Amidst these technological advancements and changing consumer expectations, a leading Tier-I supplier, representing one of the largest luxury car OEMs, decided to develop the world’s first Android-based Rear Seat Entertainment (RSE) system. This system was not only tailored for the Chinese market but also suitable for global use, and it uniquely included integration with a third-party Chinese app store, leveraging Baidu’s extensive app ecosystem.

This initiative was ambitious and aimed to position the OEM as a leader in the luxury automotive segment, aligning with the industry’s move towards more connected, user-centric vehicle environments. However, the project faced several challenges, including managing thousands of unique system requirements and coordinating a multinational team across different regulatory and technological landscapes.

Customer Problem Statement

The project’s goal was to create an Android-based Rear Seat Entertainment (RSE) system for a leading luxury car maker, targeting the Chinese market with compatibility for Baidu’s third-party app store and the Rest of the World markets. This system needed to meet growing consumer expectations for seamless connectivity and intuitive user interfaces like those found in smartphones.

Challenges

Complex Software and System Requirements: The project involved around 10,000 customer requirements, including 3,000 ones specific to software, requiring meticulous management, integration, and testing to maintain the luxury standard expected from the brand.

Multi-national Collaboration: The development spanned seven countries, involving many suppliers and needed detailed coordination to handle the complexities of a globally distributed team.

Introduction of Android in Automotive Infotainment: Customizing Android to work effectively with car-specific hardware, meeting industry standards, and ensuring security against cyber threats were significant challenges.

Cybersecurity Concerns: The integration of connectivity technologies like Wi-Fi and Bluetooth brought a higher risk of security vulnerabilities, requiring strict security measures to protect against cyber threats.

The China Challenge: Integrating a third-party Chinese Appstore added complexity, particularly in modifying software architecture to ensure both system security and functionality within the tightly regulated market.

Acsia Solution

Acsia leveraged its extensive experience in developing infotainment systems for premium automotive brands to confidently manage this complex project, ensuring timely delivery of high-quality software.

Technical Proficiency in Android Systems: Acsia adapted Android for automotive use, developing custom APIs and middleware that allowed Android to communicate effectively with the vehicle’s hardware systems.

Global Operational Capability: Acsia coordinated logistics and project management across multiple countries effectively, ensuring synchronized integration of various components.

Innovative Approach to Security: Acsia implemented high-level security measures including secure boot processes and state-of-the-art encryption, using tools like the Microsoft Threat Modelling tool to pre-emptively address potential security threats.

Comprehensive Service Offering: Acsia provided an all-in-one solution covering all aspects of development from design to final testing, streamlining the development process and reducing project complexity.

Customized Integration for Chinese Market: The integration of a third-party Chinese Appstore was tailored to meet local regulations and technical complexities, ensuring secure and functional system architecture.

Business Outcome & Impact

The successful development of one of the world’s first connected Android-based Rear Seat Entertainment (RSE) system with third-party app store integration has significantly advanced the luxury car OEM’s market position. The system not only met regulatory requirements but also exceeded consumer expectations for seamless connectivity and intuitive interfaces. Key outcomes include enhanced system security, innovative software architecture, streamlined integration and development processes, compliance with automotive standards, and efficient Android power management.

Enhanced System Security

Acsia successfully met 280 complex security requirements set by the OEM, safeguarding the infotainment system against potential cyber threats. By leveraging advanced tools like the Microsoft Threat Modelling tool, Acsia effectively identified vulnerabilities and implemented robust security measures, ensuring the protection of critical vehicle functions and customer data from unauthorized access.

Innovative Software Architecture

Acsia introduced a hypervisor architecture that allowed two operating systems to run on the same System on Chip (SoC). This innovation separated critical automotive applications from third-party apps, enhancing overall system reliability and security by isolating essential functions from less secure applications.

Streamlined Integration and Development Process

Acsia handled software development, system integration, and testing, streamlining the customer’s development process. This comprehensive approach reduced complexity and coordination efforts, potentially shortening the development timeline and lowering costs associated with managing multiple vendors.

Compliance with Automotive Standards

Acsia adhered strictly to automotive industry standards through expert Android customization and security enhancements. This ensured the infotainment system’s functionality and safety, facilitating compliance with regulatory requirements crucial for the acceptance and success of automotive products in international markets.

Android Power Management in Automotive

Acsia enabled an efficient power management system by customizing Android to meet specific automotive power management requirements. This included developing custom native and Java applications to handle power triggers, manage wake locks, control display backlight, and handle shutdown processes, aligning with automotive standards.

Key Learning

  • Complex Security Integration: Early integration of robust security measures is essential, using advanced tools to anticipate and mitigate potential vulnerabilities.
  • Innovative Use of Technology: The use of a hypervisor to run multiple operating systems on the same hardware was a key innovation, enhancing both security and system stability.
  • Adherence to Regulatory and Compliance Standards: Ongoing education and compliance with automotive and cybersecurity standards are crucial, particularly in complex markets like China.
  • Scalable and Flexible Software Architecture: Developing adaptable software architectures allows efficient customization to meet diverse market needs.
  • Enhanced Testing and Validation Processes: Robust testing frameworks are vital to ensure the system meets all operational standards, maintaining high reliability and performance.

Expert Speak

Vasanthraj G
VP Technology & Innovation
Integrating an Android-based infotainment system into a vehicle's environment posed unique challenges, particularly in terms of security and system optimization. Our expertise in customizing Android OS for automotive applications, coupled with our innovative approach to cybersecurity, enabled us to create a robust and user-friendly Rear Seat Entertainment system. This project not only showcased our technical proficiency but also our ability to adapt and innovate in response to specific market needs, such as those in China.
Anil S
VP Delivery
This project represents a landmark achievement in the automotive infotainment sector. By developing the world’s first connected Android-based Rear Seat Entertainment system with third-party app store integration, we have set a new benchmark for in-car digital experiences. Our team’s dedication to managing complex requirements and coordinating across multiple countries was instrumental in delivering this innovative solution. We are proud to have partnered with a leading luxury car OEM to bring this vision to life, enhancing the in-car experience for users globally.
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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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