Automated driving requires reliable perception of the environment to ensure the safety of the
driving task. One common perception task is 3D object detection, which aims at perceiving …

To evaluate perception components of an automated driving system, it is necessary to define
the relevant objects. While the urban domain is popular among perception datasets …

The objective of this work is to provide a comprehensive understanding of the development
of autonomous vehicle perception systems. So far, most autonomy perception research has …

To achieve safety of high level automated driving, not only functional failures like E/E system
malfunctions and software crashes should be excluded, but also functional insufficiencies …

In recent years, scenario-based testing has gained increased attention as a potentially
efficient strategy for validating the overall safety of automated vehicles. However, which …

Having efficient testing strategies is a core challenge that needs to be overcome for the
release of automated driving. This necessitates clear requirements as well as suitable …

Popular test oracles for the perception subsystem of driving automation systems identify true-
positive (TP), false-positive (FP), and false-negative (FN) objects. Oracle transparency is …

The advent of Large Language Models (LLM) provides new insights to validate Automated
Driving Systems (ADS). In the herein-introduced work, a novel approach to extracting …

Automated driving systems (ADS) are envisioned to operate in a complex open world. To
ensure that difficult environmental conditions do not trigger inadequate system behavior, it is …

Abstract Development of autonomous vehicles has hit a slump in the past years. This slump
is caused by the so-called approval trap for autonomous vehicles: While the industry has …