Trustworthy autonomous systems
To contribute to the development of trustworthy autonomous systems, SETLabs is partner in the national funding project safe.trAIn. safe.trAIn focuses on AI-based driving in regional traffic shown by the example of a driverless regional train.
For a climate-neutral and attractive transport mix, the operation of rail transport with the highest levels of automation is an essential component. According to the state of the art, this goal cannot be solved by classical automation technologies alone. On the other hand, there is remarkable progress in the development of technologies in the field of highly automated driving (on road & rail) based on the power of Artificial Intelligence (AI). A major unresolved challenge here is the linkage of AI procedures with the requirements and approval processes in the rail environment.
The use of artificial intelligence (AI) processes – such as machine learning (ML) – is currently a very promising approach to realize automated driving. In a safety-critical environment such as driverless rail transport, robust and safe AI methods are needed to be able to detect obstacles on the track with the necessary reliability.
The safe.trAIn project aims to lay the foundations for the safe use of AI for driverless rail vehicles. Thus, it addresses the greatest technological challenge for the introduction of driverless transport.
Based on the requirements for safety verification, testing methods and tools for AI-based methods are researched. A safety architecture is concretized using the example of the driverless regional train. For this use case, a GoA4 (unattended train operation) system will conceptually be developed and validated in a virtual test field.
The present project aims at the market segment of regional trains. These operate in a more open environment, in which they have to detect obstacles (such as people in the track or trees lying on the track, landslides, etc.) reliably. Compared to road transport, however, there are some factors that make the technical complexity of fully automated rail transport seem lower. Such factors are:
1. fewer interactions with other road users take place in rail operations, which occur especially in areas (e.g., stations or grade crossings) where safety can be reasonably supported by the infrastructure.
2. a collision with other road users on the track is prevented with sufficient certainty by the central control of traffic and automatic cooperation with trackside systems is established.
3. the driving function only has to be provided on a few known routes. This is why the environment can largely be assumed to be known and areas critical to the safety of driverless driving can be additionally secured if necessary.
4. unauthorized entry into the driving path is prohibited. It may even constitute a criminal offense, which greatly simplifies any traffic situations that may arise.
The underlying assumption of the project is therefore that various requirements are technically easier to solve in rail-bound traffic compared to road traffic. Thus, a timely market introduction appears more realistic. On the other hand, there are considerable differences both from a regulatory point of view and from a technical point of view (e.g., the need to use sensor technology with a longer range to observe the track, availability of suitable training data). So, the solutions, developed in the automotive sector, cannot be adopted directly. However, the ongoing research projects in the automotive industry using AI for safe environment recognition represent important preliminary work that will be taken up in this project and further developed for use in the rail sector.
Gaia-X 4 Product Lifecycle Across Automated Driving
Gaia-X represents the next generation of data infrastructure: an open, transparent, and secure digital ecosystem, where data and services can be made available, collated, and shared in an environment of trust.
SETLabs is involved in the “Gaia-X 4 Product Lifecycle Across Automated Driving” that is developing an open, decentralized data ecosystem according to Gaia-X standards. It enables the product development, production and after sales of automated driving functions, based on digital twins.
Today, many small and medium-sized enterprises repeatedly build individual interfaces for data exchange and interoperability solutions with each individual customer. This is tedious and costs money. With Gaia-X, common data-exchange mechanisms will be made available that obey to the common needs of trust.
The technological architecture of Gaia-X is used to address the following requirements:
> Interoperability of data and services: Exchange information and use exchanged information in mutually beneficial ways.
> Portability of data and services: Data is described in a standardized protocol that enables transfer and processing.
> Sovereignty over data: Participants retain absolute control over what happens to their data
> Security and trust: Gaia-X puts security technology at its core to protect every Participant and system of the Gaia-X ecosystem security by design)
Gaia-X 4 Product Lifecycle Across Automated Driving is developing an open, decentralized data ecosystem according to Gaia-X standards to enable the product development, production and after sales of Automated Driving functions, based on Digital Twins. The development will be made along two use cases: A Level 3 Automated Driving function and a sensor used within Automated Driving functions. SETLabs is focusing on collaborative System Simulation of Automated Driving using the decentralized data ecosystem and
> Considers the complete track from data to simulation
> Provides different data nodes to connect to the data ecosystem (highway testing field, test vehicle, driving simulator, system simulation environment)
> Integrates traceability and certification mechanisms for system simulation artifacts and provide simulation centred continuous integration methods, developed in the UPSIM project.
Gaia-X represents the next generation data infrastructure ecosystem based on the values of openness, transparency, sovereignty, and interoperability, to enable trust. The architecture of Gaia-X is based on the principle of decentralization where many individual data owners and technology players adopt a common standard of rules and control mechanisms the Gaia-X standard.
Today, more than 310 organizations and companies are members of Gaia-X (about 50% SMEs), they originate from different domains like mobility, energy, manufacturing, finance, agriculture, aerospace, public services, healthcare and more…