As an essential part for the development of autonomous agricultural robotics, the functional safety of autonomous agricultural machines is largely based on the functionality and robustness of non-contact sensor systems for human protection. This article presents a new step in the development of autonomous agricultural machine with a concept and the realization of a novel test method using a dynamic test stand on an agricultural farm in outdoor areas. With this test method, commercially available sensor systems are tested in a long-term test around the clock for 365 days a year and 24 h a day on a dynamic test stand in continuous outdoor use. A test over a longer period of time is needed to test as much as possible all occurring environmental conditions. This test is determined by the naturally occurring environmental conditions. This fact corresponds to the reality of unpredictable/determinable environmental conditions in the field and makes the test method and test stand so unique. The focus of the developed test methods is on creating own real environment detection areas (REDAs) for each sensor system, which can be used to compare and evaluate the autonomous human detection of the sensor systems for the functional safety of autonomous agricultural robots with a humanoid test target. Sensor manufacturers from industry and the automotive sector provide their sensor systems to have their sensors tested in cooperation with the TÜV.

Perception of the environment by sensor systems in variable environmental conditions is very complex due to the interference influences. In the field of autonomous machines or autonomous vehicles, environmental conditions play a decisive role in safe person detection. A uniform test and validation method can support the manufacturers of sensor systems during development and simultaneously provide proof of functionality. The authors have developed a concept of a novel test method, “REDA”, for this purpose. In this article, the concept is applied and measurement data are presented. The results show the versatile potential of this test method, through the manifold interpretation options of the measurement data. Using this method, the strengths and weaknesses of sensor systems have been identified with an unprecedented level of detail, flexibility, and variance to test and compare the detection capability of sensor systems. The comparison was possible regardless of the measuring principle of the sensor system used. Sensor systems have been tested and compared with each other with regard to the influence of environmental conditions themselves. The first results presented highlight the potential of the new test method. For future applications, the test method offers possibilities to test and compare manifold sensing principles, sensor system parameters, or evaluation algorithms, including, e.g., artificial intelligence.