Faculty of Engineering and Computer Science, University of Applied Sciences Osnabrück, 49076 Osnabrück, Germany.
Agromechatronic, Technische Universität Berlin, 10623 Berlin, Germany.
Sensors (Basel). 2022 Aug 1;22(15):5745. doi: 10.3390/s22155745.
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.
由于干扰影响,传感器系统在可变环境条件下对环境的感知非常复杂。在自主机器或自动驾驶车辆领域,环境条件对安全的人员检测起着决定性的作用。统一的测试和验证方法可以在传感器系统的开发过程中为制造商提供支持,同时也可以证明其功能。为此,作者开发了一种新的测试方法的概念,即“REDA”。本文应用了该概念并展示了测量数据。结果表明,通过测量数据的多种解释选项,该测试方法具有多功能的潜力。使用这种方法,可以以前所未有的详细程度、灵活性和可变性来识别传感器系统的优缺点,以测试和比较传感器系统的检测能力。这种比较可以不考虑所使用的传感器系统的测量原理。已经针对环境条件本身对传感器系统的影响对传感器系统进行了测试和比较。所呈现的第一个结果突出了新测试方法的潜力。对于未来的应用,该测试方法提供了测试和比较多种感应原理、传感器系统参数或评估算法的可能性,包括例如人工智能。