Project closure

Artificial intelligence is the key technology for safe autonomous driving

The project KI Data Tooling ends on December 31, 2023. On December 5 and 6, 2023 - the project representatives, partners and guests met at the BMW Welt in Munich for the final event. With the present results, the project has successfully contributed to significantly advancing the application of artificial intelligence (AI) in the field of autonomous driving. A comprehensive data kit now facilitates the training and validation of AI functions and opens up new possibilities for intelligent mobility solutions.

Artificial intelligence and machine learning are key technologies that are used in the field of automated and connected driving. However, safeguarding autonomous driving functions requires AI to function reliably even in difficult and usually critical situations. As part of the KI Data Tooling project, tools were therefore developed that can automatically identify critical situations that are not well covered by AI.

The research project was initiated and developed by the German Association of the Automotive Industry (VDA) and funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK). In a nationwide consortium of 17 leading companies, institutions and universities, scientists developed innovative methods and tools together with experts from industry.

Event KI Data Tooling
Tianming Qiu explains promising approaches in the AI Data Tooling project.
Event KI Data Tooling
fortiss researchers Dr. Hao Shen, Stephan Rappensperger and Tianming Qiu at the AI Data Tooling final event.

Comprehensive data kit provides new impetus

A well-founded and comprehensive database is essential for the successful use of AI-based functions in vehicles. In addition to as many scenarios as possible, this must also include various sensor modalities. Until now, however, there have only been isolated solutions for the data of individual sensor types, very limited scenarios or parts of the training and evaluation chain. In the KI Data Tooling research project, tools, and methods for the provision of data from different sensor modalities for AI-based functions were developed and investigated for the first time in their entirety.

The project focused primarily on the efficient development of the database and pursued a complete data kit for the training and validation of AI-based automated driving functions from the outset. Real, synthetic and augmented data from various sensor modalities were generated for this purpose. Methods were also developed for the analysis, quality assessment and efficient, resource-saving storage and transmission of this data. Finally, an optimized AI training strategy was developed on this basis.

Getting autonomous vehicles safely on the road with machine learning

Over the past three years, the researchers from the Machine Learning competence field worked as associated partner together with BMW Group on several challenges in the project. In these efforts, fortiss colleagues Tianming Qiu, Carlos Valle, and Stephan Rappensperger focused on data-driven methods, such as deep learning techniques, which are used for comprehensive end-to-end training of AI functions in autonomous driving.

In this context, the quality of the data is a particularly important concern. Only with such a database is it possible to successfully train and validate AI functions. However, the provision of data is associated with numerous challenges. They include various facets, which the fortiss team investigated. These include the efficient labeling of data, the integration of synthetic data and the handling of unknown or rare scenarios (corner case detection) and the generation of contextual information (long-tail scenarios).

These research contributions, which have been developed over the past three years in the team of competence field leader Dr. Hao Shen, have been integrated into the project consortium's comprehensive data tooling pipeline. There, they will significantly benefit future research and development activities in the German automotive industry.

At the high-profile final event, the fortiss researchers presented their results in detail. The event also offered the opportunity for specialist deep dives and in-depth discussions among experts to discuss future fields of research.

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