Orchestrating Agents for Automotive Research SDVerse

Format:

Book

Conference/Event

Author/Creator:

Vemuri, Pavan, author.

Conference Name:

Automotive Technical Papers (2026-01-01 : Warrendale, Pennsylvania, United States)

Language:

English

Subjects (All):

Infotainment systems.

Artificial intelligence (AI).

Computer software and hardware.

Embedded software.

Research and development.

Local Subjects:

Infotainment systems.

Artificial intelligence (AI).

Computer software and hardware.

Embedded software.

Research and development.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Automotive research landscape currently is driven by emerging technologies such as software-defined vehicles, advanced infotainment systems, and increasingly automated driving functions. This situation calls for a bigger need for efficient, comprehensive, and agile research methods. Traditional methods require significant manual effort, leading to information synthesis and dissemination bottlenecks. After doing a thorough research on how research is carried on in automotive companies, it is inferred that a lot of time is spent on gathering information and integrating it with proprietary knowledge rather than on analysis or synthesis of the information. There are tools and platforms with artificial intelligence (AI) advancement that help with deep research of a particular topic, and there are also tools and platforms that help with synthesis of proprietary information within automotive organizations. But there is a lack of a framework that dynamically integrates the aspect of deep research with the proprietary information within the organization and draws out action items and action plans for the research to be effective and efficient. The agentic AI framework introduces efficient multi-agent orchestration and seamless integration of proprietary automotive data with external research sources, incorporating principles of building effective multi-agent systems, key metrics, validation techniques, impact and also the future potential. Initial validation demonstrates a 50% reduction in research time, a 50% faster time to insight, and much more impact

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license