Android Automotive Expertise

Harnessing The Power of Android Automotive 14 for IVI
  • Configure vehicle zones, allocate displays, assign users, and route inputs
  • Customize Android Automotive 14 for your Multi-User Multi-Display-related requirements
  • Automate HMI testing for Multi-User Multi-Display
TITAN: CI/CD/CT for
Automotive ECUs
  • Highly customizable and extensible
  • Developed and deployed in-house
  • Built on Robot, Jenkins, and Python

Ushering in a New Era of In-Vehicle Experiences

Android Automotive has emerged as a pivotal force in reshaping the in-vehicle experience. By harnessing the Android applications ecosystem, the platform unlocks a plethora of functionalities and services readily accessible to drivers and passengers.
From industry-leading tools like Google Maps and Google Assistant, the platform empowers users with a familiar and intuitive interface, replicating the convenience of their everyday digital lives. By embracing over-the-air (OTA) updates and ensuring a dynamic and ever-evolving user experience through continuous innovation and feature enhancements, Android Automotive eliminates the limitations of static infotainment systems.

How Acsia Can Help?

Acsia’s Android Automotive capabilities cover the entire stack including Human-Machine Interface (HMI), vehicle networking, system health, connectivity, storage & persistence, display video & graphics, and more.

Expertise in adapting Android Automotive to OEM-specific use cases in audio, media, power management, ethernet, Bluetooth, and persistence domains.

Enabling SW development without having target HW.

Drive flawless communication and data exchange between OEM’s Android IVI system and ADAS features, enhancing safety and driver experience.

Enables faster rollout of features and a shift left approach in development with early detection of bugs.

OTA updates, keeping software current and addressing potential issues remotely.

Vast experience optimizing graphics, display, drivers, and startup for SoC; OEM-specific HW adaptations and integrations with vehicle sensors.

Expert guidance in ensuring OEM Android Automotive system adheres to industry security standards and regulatory requirements.

Create an intuitive and visually appealing HMI layer that integrates seamlessly with the Android experience, prioritizing driver focus and safety. In-house developed automated HMI testing application that can be integrated into CI/CD/CT pipelines.

Support OEM to curate a relevant and engaging app selection for the in-vehicle app store, catering to the specific needs and preferences of the target audience.

Deep knowledge in developing a platform adhering to CDD (Compatibility Definition Document) and automated test suites (CTS, VTS) to obtain certification ensuring an exceptional user experience.

Acsia’s Android Automotive capability brings to OEMs the full-throttle power to ditch limited, outdated in-vehicle infotainment systems and reimagine in-vehicle experiences by unlocking a world of personalization, intuitive interfaces, and a thriving app ecosystem.

Project Highlights

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. Defined system architecture and aligned system requirements with hardware, OEM, and regulatory needs while delivering an Android-based infotainment system with customized System UI, HMI, app store integration (including Baidu), DLT logging, lifecycle management, Cinemo-based cabin audio, custom keyboard, and AOSP browser enhancements.

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

Android version:14

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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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1. Create the HMI system, encompassing the instrument cluster, infotainment, and HMI server functionalities, with screen layouts supplied by the OEM.
2. Integrate the navigation system with the HMI framework.

Android – Version 10

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Responsible for defining the system architecture and streamlining system and software requirements (SYS2, SYS3, SWE1, SWE2) to align with the proposed hardware architecture, OEM expectations, and regulatory standards.

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Hear From Experts
NIBIL P M
AVP & Head, Advanced Technology Group
With Acsia since 2015
“As in-vehicle experiences evolve, Android Automotive OS is transforming infotainment. Acsia delivers end-to-end expertise across AOSP, HAL integration, and app development. With strong OEM and Tier-I collaborations and deep SoC experience, we enable seamless, scalable, and personalized digital cockpit solutions for the next generation of connected vehicles.”
OEM Production Program Experience:
Tier-I Experience:
Why Acsia?
Since 2017 with AAOS
Acsia has delivered Android Automotive projects since its launch by Google and Intel in March 2017.
Android Centre of Excellence
An in-house platform team focusing on productizing the AAOS platform stack (Android 13, 14 & 15) for OEMs.
Virtual Cockpit Development Environment
Acsia has invested in an AWS Graviton environment for seamless development.
Automated CI/CD/CT Pipeline
An automated CI/CD/CT pipeline for digital cockpits based on Jenkins, Zuul, and Robot; and Google test suites (CTS & VTS).
Performance Tracing & Profiling Suite
Extensible and configurable System Health Tracing & Profiling Suite built in-house enabling Boot and Application Tracing, CPU, Memory, Stack Profiling, and NVM / Flash Dump Analysis.
Acsia has delivered Android Automotive projects since its launch by Google and Intel in March 2017.
An in-house platform team focusing on productizing the AAOS platform stack (Android 13, 14 & 15) for OEMs.
Acsia has invested in an AWS Graviton environment for seamless development.
An automated CI/CD/CT pipeline for digital cockpits based on Jenkins, Zuul, and Robot; and Google test suites (CTS & VTS).
Extensible and configurable System Health Tracing & Profiling Suite built in-house enabling Boot and Application Tracing, CPU, Memory, Stack Profiling, and NVM / Flash Dump Analysis.
What’s In It For You

Android Automotive offers a familiar and intuitive interface for users accustomed to the Android ecosystem. This translates to quicker adoption and a more enjoyable in-vehicle experience, potentially boosting customer satisfaction and brand loyalty.

Leverage the power of Android’s existing codebase and infrastructure, minimizing development time and resource allocation.

By utilizing the established Android framework, OEMs can expedite the development process for in-vehicle infotainment systems, capitalize on emerging trends, and deliver cutting-edge features quicker, enhancing competitiveness.

Benefit from a vibrant app ecosystem, opening doors to new revenue streams through in-car app purchases and subscriptions.

Android Automotive embraces over-the-air (OTA) updates, ensuring the infotainment system receives continuous improvements and security patches throughout its lifespan.

Frequently Asked Questions

Android Automotive is a full-stack, open-source platform running directly on in-vehicle hardware, offering a customizable operating system for infotainment systems. Unlike Android Auto, which projects a smartphone interface onto the car’s display, Android Automotive operates independently without relying on a smartphone connection.

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Acsia offers end-to-end expertise in Android Automotive, covering customized platform development, virtualization, ADAS integration, CI/CD/CT infrastructure, OTA updates, and HMI design.

What sets the company apart is the full-stack expertise and in-house tools accelerators TITAN for efficient software deployment. Acsia has also productised Android Automotive 14. The company’s working demo showcases multi-user, multi-display capabilities, vehicle zone configurations, automated HMI testing, and advanced customization for next-gen automotive infotainment.

Watch the Android Automotive 14 Demo.

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To meet OEM-specific requirements, Acsia customizes Android Automotive across audio, media, power management, Ethernet, Bluetooth, and data persistence, ensuring seamless integration with each manufacturer’s needs.

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Acsia’s solution accelerator TITAN offers Continuous Integration, Continuous Deployment, and Continuous Testing (CI/CD/CT) tailored for automotive Electronic Control Units (ECUs). This approach streamlines the development process, enhances software quality, and accelerates time-to-market for Android Automotive applications.

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Acsia enables software development without the need for target hardware through virtualized Android platform development. The company’s solution accelerator MAYAAVI, is a virtualized development platform for Android completely deployable on AWS. This allows for more flexible and efficient development cycles, reducing costs and time associated with hardware dependencies.

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  • Rear Seat Entertainment System for a German OEM through a Japanese Tier-1.
  • Digital Cockpit for a German OEM through a German Tier-1.
  • Migration of a containerized Android 11 to Android 13.
  • Connected Cluster for an Indian Electric 2W OEM.
  • Android Embedded Device Gen 2 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 

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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