Telematics

Facilitating the connected car revolution and smarter roads.

Connected cars are taking over, driven by consumer demand and technology leaps. More and more software is increasingly becoming a reality in telematics control units (TCU) due to advanced features like V2X, increased security, requirements for Over-The-Air (OTA) updates, and a high degree of precision positioning.
V2X goes beyond comfort and safety, promising a 20% reduction in traffic snags and pollution, and another 80% in accidents. While TCU development and testing present challenges, the future is connected. It warrants expertise is protocols and systems, efficient integration and performance optimization.

How Acsia Can Help?

Knowledge of embedded systems and communication technologies, to enable remote diagnostics, connect car features, and GPS tracking.

  • Mobile Telephony and V2X Stack Integration
  • SOTA & FOTA
  • eCall (Regulatory/OEM) Stack Integration
  • Secure RUST-based UDS stack
  • Vehicle Integration
  • Cloud Data Collector
  • Protocols (MQTT, Zenoh, QUIC)

A white labelled SaaS product for smart fleet management.

  • Driver and Fleet Monitoring
  • Comprehensive Trip Management
  • Real-time vehicle data acquisition
  • Live vehicle dashboard
  • Real-time Alerts and Notifications
  • POI and Precise Navigation

Deep understanding of cellular technologies, GPS protocols, Bluetooth and in-vehicle sensors, and expertise in software, vehicle and simulation testing.

  • Vehicle & Field Testing
  • Connectivity Testing
  • Application Testing
  • Test Management
  • Integration and Test Automation
  • Certification Test
  • Over-The-Air (OTA) Testing (SOTA & FOTA)

Project Highlights

Defined system architecture and streamlined system requirements to meet the proposed hardware architecture, OEM requirements and regulatory requirements.

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  1. Telematics Platform Core Component Architecture development.
  2. Facilitated the assessments and road map approach through workshops.
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Hear From Experts
DILJITH MUTHUVANA
Head of Telematics & Connectivity Solutions
With Acsia since 2015
“As telematics and V2X become table stakes in the connected car era, they are inadvertently ushering in the age of Intelligent Transportation Systems. This emerging outcome necessitates the convergence of technologies from OEMs, Tier-I suppliers, telecom providers, insurers, and software developers. For a decade, Acsia has collaborated with these stakeholders to enhance the safety of drivers and passengers, ensure compliance with regulators, and facilitate seamless integration with other safety and driver assistance systems.”
OEM Production Program Experience:
Tier-I Experience:
Why Acsia?
Telematics Data Management & Analytics Expertise
Proven ability to securely collect, manage, and analyze data from connected vehicles unlocks insights for fleet management, predictive maintenance, and personalized services.
Cloud Integration and Big Data Analytics
Expertise in integrating Telematics and V2X data with secure cloud platforms enables scalability, advanced analytics, and future service development.
Functional Safety & Cybersecurity Certifications
Mastery of functional safety standards (ISO 26262) and cybersecurity protocols ensure sensitive vehicle and user data transmitted through Telematics and V2X systems remain protected.
Agile Development & Global Delivery
Proven experience in agile development methodologies and the ability to deliver projects through a global talent pool ensure efficient project management, cost optimization, and faster time-to-market.
Regulatory Compliance Expertise
A thorough understanding of evolving regulations and standards governing connected vehicles' data privacy and safety, such as GDPR and ISO standards, ensures 100% compliance and avoids legal roadblocks.
State-of-the-art Telematics Testing
World-class test infrastructure for executing V2X testing supporting DSRC, C-V2X, and AR-V2X, application testing, and real-time/offline feature validation using big data and cloud.
Proven ability to securely collect, manage, and analyze data from connected vehicles unlocks insights for fleet management, predictive maintenance, and personalized services.
Expertise in integrating Telematics and V2X data with secure cloud platforms enables scalability, advanced analytics, and future service development.
Mastery of functional safety standards (ISO 26262) and cybersecurity protocols ensure sensitive vehicle and user data transmitted through Telematics and V2X systems remain protected.
Proven experience in agile development methodologies and the ability to deliver projects through a global talent pool ensure efficient project management, cost optimization, and faster time-to-market.
A thorough understanding of evolving regulations and standards governing connected vehicles' data privacy and safety, such as GDPR and ISO standards, ensures 100% compliance and avoids legal roadblocks.
World-class test infrastructure for executing V2X testing supporting DSRC, C-V2X, and AR-V2X, application testing, and real-time/offline feature validation using big data and cloud.
What’s In It For You

Acsia’s expertise in telematics data management and V2X communication unlocks valuable insights from connected vehicles leading to improved fleet management, predictive maintenance, and personalized services.

Acsia’s V2X communication software facilitates car-to-car and car-to-infrastructure communication, enabling real-time data exchange for safer roads, reduced traffic congestion, and improved accident prevention.

Benefit from Acsia’s pre-developed software components and efficient processes to accelerate time-to-market for your Telematics and V2X solutions while reducing development overhead compared to in-house efforts.

Acsia’s solutions enable secure cloud integration for connected vehicle services ensuring scalability and robust security for your telematics and V2X data.

By engaging Acsia, you gain access to cutting-edge technologies that support future advancements in connected and autonomous vehicles, keeping your platform at the forefront of the mobility revolution.

Acsia’s Telematics and V2X solutions can pave the way for innovative connected car services like usage-based insurance, personalized navigation, and real-time diagnostics, opening up new revenue streams.

Acsia’s in-depth knowledge of functional safety standards, data privacy, and cybersecurity regulations ensures absolute compliance and zero hassles with regulatory authorities.

Frequently Asked Questions

Telematics integrates telecommunications and informatics within vehicles, enabling features like real-time diagnostics, GPS tracking, and vehicle-to-everything (V2X) communication. These advancements enhance safety, efficiency, and connectivity on the road.

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Acsia offers Telematics ECU Development, XACT Fleet Telematics, and Telematics Testing Services, ensuring advanced connectivity, fleet management, and reliable telematics solutions.

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Acsia offers expertise in embedded systems and communication technologies, assisting in TCU development and testing. The company’s services include mobile telephony and V2X stack integration, Over-The-Air (OTA) updates (SOTA & FOTA), eCall integration, secure RUST-based UDS stack, vehicle integration, cloud data collection, and support for protocols like MQTT, Zenoh, and QUIC.

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XACT Fleet Telematics is Acsia’s white-labeled SaaS product designed for smart fleet management. It offers features like driver and fleet monitoring, comprehensive trip management, real-time vehicle data acquisition, live vehicle dashboards, real-time alerts and notifications, and precise navigation with points of interest (POI).

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Vehicle-to-Everything (V2X) technology enables vehicles to communicate with each other and with infrastructure, enhancing safety and efficiency. It promises a 20% reduction in traffic congestion and pollution, and up to an 80% decrease in accidents, by facilitating real-time information exchange and proactive responses to road conditions.

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  • TCU System Architecture Development & Support for a German Tier-1 (EU OEM).
  • Generic Telematics Platform Consolidation for a German Tier-1.
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Request a Meeting

Download the Datasheet
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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