From GPS to V2X: The Evolution of Telematics and the Connected Car
by Diljith Kunnamcherry Muthuvana
An advanced cityscape showcasing vehicles equipped with V2X communication technology, enhancing safety and traffic efficiency through connected telematics solutions.
Acsia’s V2X solutions enhance vehicle connectivity, enabling communication with infrastructure, other vehicles, and pedestrians for improved safety and efficiency.

In Brief

  • Telematics has transcended basic GPS tracking, ushering in an era of sophisticated vehicle connectivity.
  • Vehicle-to-Everything (V2X) communication stands as a pivotal advancement in transportation, facilitating direct interactions between vehicles and their environment to greatly enhance safety and efficiency.
  • Acsia Technologies is a leading player in developing cutting-edge V2X solutions, leveraging extensive expertise in automotive software and embedded systems.

The automotive landscape is undergoing a profound transformation, driven by a convergence of connectivity, data-driven insights, and intelligent systems. At the heart of this revolution lies telematics, the technology that enables vehicles to communicate with the world around them. While GPS-based tracking laid the foundation for telematics, the industry has since evolved dramatically, culminating in the advent of Vehicle-to-Everything (V2X) communication.

The Evolution of Telematics: From Location Tracking to Comprehensive Connectivity

In its nascent stages, telematics primarily served as a means to track vehicle location and monitor fundamental parameters such as speed and fuel consumption. This data proved invaluable for fleet management and logistical operations, but the true potential of telematics remained untapped. With the maturation of cellular networks and cloud computing, telematics expanded to encompass a broad spectrum of services, including:

  • Remote Diagnostics & Prognostics: Telematics now empowers mechanics to remotely diagnose vehicle health, leveraging real-time data to identify potential issues before they escalate into costly breakdowns. Advanced algorithms even enable predictive maintenance, anticipating failures and optimizing maintenance schedules.
  • Over-the-Air (OTA) Updates: The days of mandatory trips to the dealership for software updates are fading. Telematics enables seamless, over-the-air updates for firmware, software patches, and even new features, enhancing vehicle functionality and security.
  • Usage-Based Insurance (UBI): Telematics data can revolutionize insurance models by tailoring premiums based on individual driving habits. By incentivizing safe driving behaviour, UBI can contribute to safer roads and reduced insurance costs for responsible drivers.

The Rise of V2X Communication: A Paradigm Shift in Automotive Safety and Efficiency

V2X technology represents a paradigm shift in automotive communication. By enabling communication between vehicles (V2V), infrastructure (V2I), and vulnerable road users like pedestrians and cyclists (V2P), V2X technology unlocks numerous opportunities for improving safety, efficiency, and the overall driving experience.

  • V2V Communication: Imagine a scenario where vehicles exchange real-time information about their speed, position, and trajectory. This data can empower collision avoidance systems, cooperative adaptive cruise control, and other advanced driver assistance systems (ADAS), significantly reducing the risk of accidents.
  • V2I Communication: Through Vehicle-to-Infrastructure (V2I) technology, vehicles can receive crucial information from roadside systems, including real-time traffic signal timing, road hazards, and optimal route suggestions. This enhances traffic flow, reduces congestion, and improves driver awareness, leading to safer and more efficient journeys.
  • V2P Communication: In the quest for safer roads, Vehicle-to-Pedestrian (V2P) communication allows vehicles to alert pedestrians and cyclists of their presence, especially in low-visibility situations. This technology significantly enhances safety for vulnerable road users by providing timely warnings and preventing potential accidents.

Acsia Technologies: Pioneering the V2X Revolution

Acsia Technologies is at the forefront of developing V2X solutions that leverage the latest advancements in wireless communication, sensor fusion, and artificial intelligence. Our team of seasoned engineers possesses a deep understanding of the automotive ecosystem, enabling us to craft solutions that seamlessly integrate with existing vehicle systems.

Our V2X Portfolio:

  • Onboard Units (OBUs): Compact, high-performance devices that equip vehicles with V2X communication capabilities.
  • Roadside Units (RSUs): Infrastructure-based devices that broadcast critical safety and traffic information to vehicles.
  • V2X Software Stack: A comprehensive software solution that enables a wide range of V2X applications and services.
  • Data Analytics Platform: A robust platform that leverages V2X data to generate actionable insights for traffic management, road safety initiatives, and autonomous vehicle development.

The Road Ahead

As V2X technology evolves, its potential to transform how vehicles interact with each other and their surroundings becomes increasingly clear. From enabling safer roads to building more efficient traffic ecosystems, the possibilities are vast. At Acsia Technologies, we’re proud to be part of this journey — exploring, innovating, and contributing to the future of mobility.

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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.
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  • 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.
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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.
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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.
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Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
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  • 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.
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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.
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Build an AI-powered log analytics assistant that can:

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Goal

Deliver a working prototype that:

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Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
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Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
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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).
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  • 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.
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Impact

  • Safer driving experience with fewer distractions.
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  • 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.
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  • 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.
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  • 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).
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  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
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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).

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Build a Generative AI assistant that takes as input:

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  • 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.
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Impact

  • Faster project staffing → quicker project kick-offs.
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  • Lower staffing costs through data-driven optimization.
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