What are the key ideas shaping the current automation landscape? Read on to learn about three insights that emerged from discussions between participants at Fraunhofer IKS’ Safe Intelligence Day 2025.

As the railway industry advances, integrating Artificial Intelligence (AI) systems has become essential to improve efficiency and enhance the attractiveness of rail transport. However, implementing AI in railway operations comes with several challenges. Ensuring safety is the top priority while simultaneously increasing throughput.

Quality assurance is one of the, if not the most crucial competitive factors for industrial companies. German companies have long since recognized this and generally do not have to contend with quality issues. But that comes at a cost. Artificial intelligence is now believed to become a game changer in quality assurance. Dr. Niels Syassen, Executive Board member with responsibility for SICK AG’s Technology & Digitalization portfolio, explains how this will work.

There are so many myths about autonomous driving and safety in the public debate. We're going to look at four of them here from a US perspective.

Autonomous driving can work well in precisely defined areas of use. Yet if driverless cars are let loose into the free-for-all of everyday road traffic unprepared, difficulties can crop up that these vehicles are not prepared to cope with. Three examples.

Complex behavior presented by modern vehicles relies on complex sensors recognizing the environment of the vehicle. Unfortunately, this functionality does not operate in a black-white scheme.

High-performance computers (HPC) as the central elements of E/E-architectures in software-driven vehicles need an adequate operating system solution. Linux is a good choice for HPCs.

The demanding integrity and availability requirements of Highly Automated Driving need to be addressed by suitable architectures.

The days of hollow “women's power” phrases are over. This has long since become a social demand for equal opportunities for women, which is clearly formulated, emphatically demanded and often put into practice - albeit with varying degrees of commitment. Jessica Kelly, who has been a researcher at the institute since 2022, explains what it looks like in science and at Fraunhofer IKS.

Algorithms used in automated driving systems are complex and use non-deterministic deep neural networks. Some variants of deep neural networks can be explained theo­retically but their behavior under all conditions for the set of safety-critical scenarios to be covered by the automated driving system in the given operational design domain is generally not readily understood. Here is how it could work – a new approach.