In 2017, regulations specifically addressing conditional vehicle automation were added to the Federal Road Traffic Act in Germany. In a further quantum leap, legal provisions for highly automated cars were introduced in 2021. The intention of the …

Partial and conditional driving automation systems are still relying on human capabilities and set minimum requirements on the corresponding driver state. In the FAT project “DySAM - Dynamic measurement and assessment of situation awareness in …

Drivers are generally good at anticipating driving situations. They use implicit cues, such as lateral and longitudinal movements of other vehicles and contextual cues such as characteristics of the prevailing situation. These cues also help …

The perception system is one of the important components of autonomous vehicles, as it provides the information that is required by vehicle control to make decisions on the manoeuvre of the vehicle. The study focuses on the development of target …

The concept of the Guardian Angel system represents a pivotal advancement in vehicular safety, with a focus on enhancing the driving experience for individuals with diminished driving skills, particularly elderly drivers seeking to retain their …

This edited book describes novel human-systems integration approaches to improve acceptance, safety, and comfort of automated vehicles. Each chapter analyses different aspects in the development of automated driving systems such as: assessing …

With the increasing complexity of automated driving systems, formal verification as well as statistical verification that solely relies on real-world testing methods, become infeasible. Virtual testing seems like a promising alternative to …

Automated Driving (AD) technologies are transforming the mobility sector, promising enhancements in efficiency and safety as well as user experience tailored to diverse user needs. As the sector moves toward a more automated ecosystem, it's …

Research and development of high levels of automated driving (AD) vehicles has received considerable attention in recent years. Thereby, for the time being, humans will remain actively involved to assure overall safety, whether as drivers, safety …

This research explores an emerging approach, the adversarial policy learning paradigm, that aims to increase safety and robustness in deep reinforcement learning models for automated driving. We propose an iterative procedure to train an …

Automated driving functions have become firmly established in the automotive industry. Particularly parking maneuvers lend themselves comparatively well to automation due to the low velocity and the small operating space. However, to be accepted …

An Automated Driving System (ADS) must undergo comprehensive safety testing before receiving a road permit. Since it is not clear what exactly constitutes sufficient safety for an ADS, one could assume that an ADS is safe enough if it is at least …

The development of automated driving (AD) is a continuously advancing process with gradual introduction of limited functionality, which today enables

Online deep reinforcement learning training poses challenges due to its length and instability, despite the development of learning algorithms targeted to overcome these issues. Offline learning has emerged as a potential solution, but it …