Choose another country to see content specific to your location

//Select Country

Automotive E-SSENTIALS

Your regular update for technical and industry information

The openGENESIS collaboration released first results how to achieve reliable AI data

Digital Transformation GenesisThe openGENESIS collaboration platform for the assessment of Artificial Intelligence finished its first spotlight project "A reliable AI data labeling process". openGENESIS is hosted as a working group within the Eclipse Foundation.

 

As part of this project Incenda AI GmbH and TÜV SÜD published a white paper presenting a lifecycle and development process for AI systems, with a special focus on data quality and creating high quality labels.

 

This publication marks the first freely available process for labeling data for machine learning, a prerequisite for developing high quality AI systems based on data. Therefore, openGENESIS took the first step towards safe and trustful AI, an important requirement for autonomous vehicles.

 

For further information, and for discussing this topic with us, please do not hesitate to contact our TÜV SÜD expert Patrick Scharpfenecker directly.

 

Download our white paper

 

White paper abstract:

Due to its wide success in recent years, Artificial Intelligence (AI) is being used in more and more systems. As established Software Engineering practices, including development processes, fail to capture the complexity and additional challenges of developing AI systems, many Software Developers struggle using AI, especially in safety critical areas like healthcare or automotive.

One of the AI methods with the highest impact so far is supervised Machine Learning (ML). The performance of supervised ML is determined to a large extent by the data used to train and evaluate the developed models and the application of established Software Engineering practices. Common issues include data and label quality, immature frameworks and processes for supervised ML development, a lack of traceability of requirements to implementation and limited transparency of some models.

The contribution of this whitepaper is the discussion and establishment of a sound supervised ML lifecycle, with a focus on data quality, from the intent of developing a system using supervised ML to the decommissioning of the developed system. The different steps of the lifecycle are detailed and a deep-dive into the labeling steps is provided by defining a labeling process. The discussion includes activities that are recommended to be performed in order to create high quality labels and raises typical issues during labeling.

EXPLORE

Automated driving requires international regulations
White paper

Automated driving requires international regulations

A look at the current state of developments

Homologation of Automated Vehicles: The Regulatory Challenge
White paper

Homologation of Automated Vehicles: The Regulatory Challenge

A six-point approach for developing a regulatory framework.

Read More

Cyber security threats of autonomous and connected vehicles
Infographics

Cyber security threats of connected vehicles

Consequences and safety solutions

Automotive Essentials
E-ssentials

Automotive E-ssentials

Gear up for safety and success in the automotive & transportation industry.

Learn more

Automotive wireless connectivity
Stories

Keeping it connected: Wireless technology for automotive

Ensure road safety with increasing connectivity.

Learn more

Assessment of automated vehicles with scenario-based testing
Infosheet

Scenario-based testing for autonomous vehicles

Verify your automated driving functions via proving ground tests

View all resources

Next Steps

Select Your Location

Global

Americas

Asia

Europe

Middle East and Africa