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Acsia Joins the BMW Group and Industry Partners to Demonstrate ASA Motion Link at AutoSens Europe in Barcelona

Oct 12, 2024
Rahul V R, Senior Specialist at Acsia Technologies, at the BMW booth during AutoSens Europe 2024 in Barcelona, representing Acsia in the ASA Motion Link demonstrations.
Rahul V R of Acsia Technologies at the BMW booth during AutoSens Europe 2024 in Barcelona.

The joint showcase highlighted how ASA Motion Link is driving high-speed data transmission, interoperability, and the shift toward software-defined vehicles.

Thiruvananthapuram, India | October 12, 2024: Acsia Technologies, a leading provider of automotive software powering Digital Cockpits & Displays, e-Mobility and Telematics, joined the BMW Group and technology partners at AutoSens Europe in Barcelona to demonstrate how ASA Motion Link (ASA-ML) technology can make vehicles safer and more connected.

At the BMW booth, partners showcased the Multi-Image Sensor Zonal Aggregator, featuring Microchip’s VS775S and VS776Q components. This advanced solution enhances vehicle imaging and data processing, providing greater precision and responsiveness in safety-critical applications, while paving the way for future autonomous driving capabilities.

The event also featured the debut of the ASA Motion Link Proof of Concept, highlighting seamless high-speed video transmission using the ASA-ML protocol. Demonstrations illustrated interoperability between multiple vendors’ solutions and featured a centralised graphical user interface (GUI) enabling real-time control of camera functions and streaming.

Representing Acsia at AutoSens, Rahul V R, Senior Specialist, collaborated with automotive experts and partners throughout the event, supporting live demonstrations and addressing technical challenges that ensured a successful showcase. The demonstrations were facilitated by BMW Group team members – Kirsten Matheus, Christina Hein and Germán Ferreira Peña, who helped create a dynamic environment for collaboration and innovation.

Highlighting the collaborative effort, Germán Ferreira Peña, Specialist E/E at the BMW Group, said: “The AutoSens event in Barcelona generated strong interest in the Proof of Concept. I would like to congratulate Rahul and Shabna for their critical support during development and delivery. Rahul managed the project’s technical challenges under pressure and solved an issue on-site that made it possible to present the most important setup of the PoC.”

The demonstrations underscored the convergence of imaging and software intelligence that is reshaping mobility. By integrating high-speed data transmission with intelligent software platforms, ASA-ML is paving the way for Ethernet-driven zonal architectures – an essential building block for software-defined vehicles.

Commenting on the participation, Jijimon Chandran, Founder & CEO of Acsia Technologies, said: “ASA Motion Link is a collective industry effort. We are proud to contribute our expertise in software integration and user-centric platforms to this ecosystem. It is through collaborations like these that we advance towards safer, more connected, software-defined vehicles.”

About Acsia

Acsia Technologies is a leading provider of automotive software powering Digital Cockpits & Displays, e-Mobility and Telematics. We use our expertise across AUTOSAR, Android Automotive, Automotive Linux, QNX, HMI, Middleware and Platform Development, CI/CD/CT, AI/ML, Verification & Validation, Cybersecurity, Functional Safety, and Performance Optimisation, to develop solutions that simplify complex problems and create safer, sustainable, and more compelling driver and passenger experiences. With a presence across the United States, Germany, Japan, and India, we collaborate with top automobile manufacturers and Tier-1 suppliers.

For more information, visit www.acsiatech.com

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Athul Lal A G
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Email: athul.lal@acsiatech.com
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AH2025/PS06 | AI/ML

Context

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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.
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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.
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Pain Point

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Goal

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Outputs

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Impact

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Pain Point

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  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

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  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

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  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

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  • Improved accessibility and inclusivity for diverse user needs.
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Context

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Pain Point

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  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
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Challenge

Build an AI-powered project management assistant that can:

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  • 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).
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  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

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Context

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Pain Point

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  • 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

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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

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Impact

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