Vehicle Usage Digital Twins: Mobility Analysis Test Case Applus IDIADA Group

Format:

Book

Conference/Event

Author/Creator:

Arturo, Rubio, author.

Contributor:

Diaz, Francisco

Marín Saltó, Anna

Olivencia, Sergio

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

In the evolving landscape of the automotive industry, this study presents an innovative approach to developing digital twins for driver profiles, establishing a standardized and scalable procedure for collecting and analyzing driving data on a global scale. The proposed methodology centers on the development of a robust cloud infrastructure, including Data Lake and associated services, designed for efficient storage and processing of large volumes of data from multiple markets and vehicle types.The research introduces an adaptable procedure for data collection campaigns, applicable to diverse global markets and encompassing a wide range of vehicles, from internal combustion engines to electric and hybrid models. A key feature of this approach is the establishment of advanced data decoding protocols, enabling precise interpretation of CAN network information from vehicles of different manufacturers and models, even when the CAN structure is not previously known.The study defines standardized parameters for data recording, ensuring comparability across different markets and vehicle types, while developing adaptive analysis methodologies to identify specific driving patterns based on vehicle segment, propulsion technology, and demographic characteristics. This comprehensive approach is underpinned by a framework that ensures compliance with data protection regulations globally, facilitating ethical and legal data management across different jurisdictions.The anticipated outcomes include the creation of a highly flexible data-as-a-service platform capable of integrating and analyzing driver data worldwide, and the establishment of a standardized procedure for characterizing driver profiles. The development of advanced vehicular data decoding capabilities allows for the inclusion of a wide variety of brands and models, enabling truly global insights into driver behavior.This study lays the groundwork for a global understanding of driver behavior, providing automotive manufacturers with a powerful tool to adapt their designs to the needs of users worldwide, accelerating innovation in vehicle design and improving the safety of future vehicles

Notes:

Vendor supplied data

Publisher Number:

2026-26-0394

Access Restriction:

Restricted for use by site license