Coverage and interoperability of ASAM standards in an endpoint-2-edge-2-cloud application

The new ASAM Guide is essential reading for anyone involved in vehicle development. Many thanks to Dr. Ralf Nörenberg and Dr. Frank Hantschel for the exciting article (pp. 56-57) on the integration of ASAM standards in the RepliCar project!

ASAM GUIDE

The ASAM:GUIDE is a comprehensive reference to ASAM standards, ASAM technologies, ASAM members, and ASAM-compliant products. This guide is designed to be your go-to resource for understanding how ASAM supports the future of vehicle development and testing.

The guide provides an overview of ASAM’s complete standards portfolio and shares insights into how these standards interconnect and complement each other. The numerous application stories provided by our members give you a great impression of what can be achieved if you adopt standards and if you actively engage in their development. [by ASAM e.V.]

Challenges

Safe perception of the environment and accurate localization of the ego perspective within the environment are two crucial prerequisites for the virtual validation of automated driving vehicles. The BMWK-funded joint project RepliCar (reference sensor technology for high-precision sensor validation for automated driving) is recording a highly accurate representation of reality as a comparative value for the validation.

Solution

RepliCar aims to integrate sensory ground truth data directly into the simulative validation. This includes data acquisition, data post-processing, feature extraction, and data fusion through to the object list. The project will rely on proven ASAM standards for data artifacts and interfaces and investigate their interoperability within an end-to-end toolchain for homologation. A GAIA-X-related data space enables partners to trustfully exchange information while keeping their intellectual property.

Key benefits

Replicar is a research project to improve processes of virtual validation of vehicles based on real world data. Explicitly, creating a new level of high-resolution ground truth supports investigating how test procedures must be adapted to validate the sensors‘ reliability. This is a necessity for homologation and avoidance of perception and display errors in the sensors.

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