Autonomous driving

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

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.

A precisely defined operating environment is not only important for autonomous driving. The Operational Design Domain (ODD) also ensures safety for many other highly automated systems in rail transport, logistics and mobile robotics.

This year's two-day Safetronic conference will once again focus on a wide range of presentations to the holistic safety of road vehicles. Frank Kirschke-Biller, member of the program committee, discusses focal points and highlights for participants in an interview.

In many major cities, the regular operation of self-driving trains is already part of everyday life. But how does autonomous driving in rail transport work?

It takes time. And it will take time. But it will come: Fraunhofer IKS is making a decisive research contribution to autonomous driving, explains Dr. Reinhard Stolle, the new head of the Mobility business unit and deputy director of the institute. But there is still a lot to be done.

Autonomous systems are increasingly being used in various safety-critical applications, such as autonomous driving. Especially here, the environment and operating conditions are of crucial importance. Machine Learning components (e.g., Deep Neural Network (DNN) serve as enablers and therefore themselves must continue to operate reliably despite changing environmental conditions. Fraunhofer IKS provides a solution that handles such diverse and continuously changing environmental conditions.

Assuming we knew how to properly assess the safety of an autonomous driving application, what would be the consequences? A preview of my presentation at the Safetronic conference (November 15th and 16th 2023).

Holistic safety for road vehicles is the focus of Safetronic conference (15-16 November 2023 in Leinfelden-Echterdingen). In the video interview, Fraunhofer IKS Institute Director Prof. Dr. Mario Trapp and program committee member Hans-Leo Ross from Vay Technologies discuss safety challenges in the automotive sector.

Andreas Knapp is a member of the program committee for Safetronic 2023, the international conference on holistic safety for road vehicles. In this interview, he discusses the content that the participants can expect to enjoy and the things he is particularly looking forward to.

The development of autonomous driving (AD) technologies has reached the stage where the safety of such systems is a dominating factor in defining their success. For verification and validation of autonomous vehicles in a fixed operational design domain (ODD), simulation-based testing is one of the highly recommended methods for modular testing of planning and control systems.

In our previous article, we introduced some standardized terminology related to Autonomous Driving and Operational Design Domain (ODD). In this article, we look at the safety implications of transitioning through multiple ODDs while executing the Dynamic Driving Task (DDT).

The standardized architecture of the Fraunhofer IKS integrates any automated driving function into an existing automated verhicle.

Using artificial intelligence (AI) in the rail industry could potentially increase both efficiency and quality. At a workshop hosted by Fraunhofer IKS, experts from Deutsche Bahn, Siemens Mobility and the institute itself gathered to discuss the possible uses and challenges associated with AI in rail applications, as well as strategies for ensuring system safety.

22 abstracts were submitted for this year’s Safetronic conference (November 8–9, 2022), with the Program Committee selecting the best of them. Simon Fürst, Cooperation Management Automated Driving at BMW Group, Munich, and Conference Co-chair, explains the topics Safetronic will focus on as well as the highlights visitors can expect.

The international conference Safetronic holistically addresses the safety of road vehicles. Abstracts for conference papers may be submitted until May 9, 2022.

As farmers step up their use of information and communication technology, agriculture is not only becoming more efficient, but also more sustainable and resilient. A Fraunhofer IKS webinar explored some different solutions and strategies in the field of smart farming.

The system in autonomous vehicles must be able to reliably detect pedestrians. Deep learning approaches are the main method used for this task. However, in comparison with classic software, the results of these approaches must also be checked and verified, which requires various complicated technical measures. This article provides an overview of these measures.

In the field of modern warehouse logistics, speed is crucial to success. This is where autonomous mobile robots (AMR) come in. In order to perform their tasks, they must respond as flexibly as possible to new environmental conditions and warehouse goods. ResilientSOA, the framework developed by Fraunhofer IKS, provides the necessary prerequisites. In this interview, project manager Florian Wörter explains exactly how it works.

For autonomous vehicles to move smoothly and safely through road traffic in the future, they must be able to communicate with other road users and with the traffic infrastructure. To support the communication, Fraunhofer IKS has developed a new approach for such hybrid perception.

When ensuring the safety of automated driving systems, it is important to take into account technological as well as societal and legal aspects.

As we move toward the “smart city”, sensors are set to also collect data on air quality, traffic, etc., and — in the long term — provide us with cleaner air and help us get where we want to be faster. The Fraunhofer Institute for Cognitive Systems IKS offers the necessary technologies for the safe operation of artificial intelligence (AI) and provides cities and communities with a framework to help them get started.

A sticker on a give way sign. Branches hanging in front of a stop sign. Graffiti on a speed limit sign. These are all completely normal sights on our roads, aren’t they? But things that wouldn’t generally be a problem for humans can really make life difficult for artificial intelligence.

Will packages be delivered by anonymous robots in the near future? And when will we be able to take an air taxi to town? We will encounter autonomous means of transport more and more frequently in our everyday lives as well. An overview of the current status of a variety of autonomous vehicles.

When we think about autonomous vehicles, we usually think of self-driving cars where people are mere passengers. In the business domain, however, their potential lies in logistics. New technologies could fundamentally change the transportation of goods, making it safer and more efficient. An overview of the current status of a variety of autonomous vehicles in industry.

A simulation designed by Fraunhofer IKS paves the way for robots and humans to interact safely — without reducing efficiency.

Artificial intelligence (AI) has to be able to handle uncertainty before we can trust it to deliver in safety-critical use cases, for example, autonomous cars. The Fraunhofer Institute for Cognitive Systems IKS is investigating ways to help AI reason with uncertainty, one being the operational design domain.

Science needs to prove that systems based on artificial intelligence (AI) are safe enough. That is what Prof. Simon Burton, who recently became Division Director for Safety at Fraunhofer IKS, is calling for. In this interview he explains the institute’s approaches to ensuring safety.

On the automotive market, traditional manufacturers are in a head-to-head race with new players from the software world. In the end, the winner will be decided not only on the basis of technological innovation, but also by how secure the technologies are.

Addressing the wide range of challenges in dependable cloud-based cyber-physical systems of systems (CPSoS) requires new approaches that are automated and efficient enough to shift some of the system’s elements to runtime. To design and evaluate the system architecture, Fraunhofer IKS has developed an iterative and automated process.

The future will rely on widespread, massively-connected, highly-intelligent systems. To implement this vision, we need dependable cloud-based cyber-physical systems. Research in this direction encounters as many opportunities as challenges. Such systems exist in changing environments and interact with other systems and humans, which requires intelligence, autonomy, safety and adaptability – in fact, conflicting goals. An example of this is autonomous driving.

Machine learning means a disruptive challenge for safety assurance. Without an acceptable safety assurance concept, many great ideas will not find their way to the market. It’s therefore no surprise that AI safety has gained much more attention over the past few months. A brief series of articles will take a closer look at the importance of safety for AI. The first article focuses on the basic understanding of what safety actually means. It highlights why it is so important to understand that safe AI has less to do with AI itself and much more to do with safety engineering.

Machine learning, and especially deep learning, can be used to enable many highly complex applications, such as autonomous driving. However, there are new challenges that need to be overcome to secure such systems. An overview.

Today, autonomous vehicles function reasonably well in test situations since the conditions are severely restricted and thus easy to manage. A key issue however is how to design autonomous vehicles so that they operate dependably even in complex and previously unknown situations. A solution from Fraunhofer IKS is helping to uncover and predict such difficult situations.

Complex machine learning (ML) processes present researchers with a problem: Eliminating errors before autonomous systems are put into operation and reliably identifying errors at runtime are laborious and challenging processes. At present, this severely restricts the use of machine learning in safety-critical systems and necessitates new approaches in order to benefit from the advantages offered by ML in these areas in the future.