Acsia and the BMW Group Present Joint Keynote on Future of In-Vehicle Networking at Automotive Ethernet Congress 2025
Feb 21, 2025
Patrice Ancel of the BMW Group and Sojan James of Acsia Technologies speak during their joint keynote on ASA-ML and RCP at the 11th Automotive Ethernet Congress 2025 in Munich.
Patrice Ancel of the BMW Group and Sojan James of Acsia Technologies delivering their joint keynote on ASA-ML and RCP at the 11th Automotive Ethernet Congress 2025 in Munich.

Their keynote highlighted how ASA-ML and Remote-Control Protocol can reshape vehicle data management and enable the next wave of software-defined mobility.

Munich, Germany | 21 February 2025: Acsia Technologies, a leading provider of automotive software powering Digital Cockpits & Displays, e-Mobility, and Telematics, joined global industry leaders at the 11th Automotive Ethernet Congress (AEC) 2025, sharing insights on the future of in-vehicle networking and software-defined vehicle architectures alongside the BMW Group in Munich.

Patrice Ancel, Head of In-Vehicle Networking Technologies at the BMW Group, and Sojan James, Principal Architect at Acsia Technologies, delivered a joint keynote on “Data Aggregation and Edge Node Control using ASA-ML and RCP for Zonal Architecture.”

“RCP, the Remote Control Protocol, seemed to be a topic of interest for many OEMs,” noted Sojan James. “The ability to have very lightweight edge nodes, some even without software, was particularly fascinating. ASA-ML is already production-ready and evolving to include Ethernet. The next few years will see significant action around ASA-ML and 802.3DM as these standards converge to deliver the combined benefits of ASA-ML and Ethernet.”

Patrice Ancel highlighted the industry’s momentum: “There is still a lot of exciting work to be done on these networking technologies, and we should be proud that Acsia is playing a role in this industry transformation. We are actively contributing to both ASA and Open Alliance.”

“Our engagement with the BMW Group at AEC 2025 reinforces Acsia’s focus on Ethernet-driven zonal architectures – an essential step in the evolution of software-defined vehicles. We are proud to help shape the technologies that will power the next generation of intelligent mobility.”

Laying the Foundation for Software-Defined Vehicles

The shift to Ethernet-driven zonal architectures represents more than a reorganisation of vehicle networks. It is a fundamental rethink of how data moves, how decisions are made at the edge, and how software is becoming the driving force in modern vehicles.

Traditional distributed ECU designs are reaching their limits as software complexity grows. Zonal architectures – supported by ASA-ML (Automotive SerDes Alliance – Machine Learning) and RCP (Remote Control Protocol) – offer a more scalable path forward:

ASA-ML enables Ethernet-based, high-speed, secure and asymmetric data transfer, balancing performance and cost while integrating seamlessly into evolving vehicle architectures.

RCP introduces a centralised control approach, simplifying edge node management, reducing hardware complexity and maintaining network efficiency.

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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Email: athul.lal@acsiatech.com
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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.
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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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Impact

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Pain Point

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Goal

Deliver real-time, adaptive personalization of:

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Outputs

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Impact

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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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  • 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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Context

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Pain Point

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Challenge

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Goal

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