**1. Introduction**

In the field of driver assistance and active safety systems, an increasing level of automation has been introduced in public transport in recent years [1]. Automated vehicles will be able to detect and react to hazards and vulnerable road users faster and more appropriately than a human driver. This is expected to lead to a significant reduction in road accidents, as around 90 percent of accidents are primarily caused by human error, e.g., [2,3]. Taking the driver out of control will improve driving comfort by reducing the driver's workload, especially in monotonous situations, such as traffic jams, where the driver is mentally under-challenged, or in cases where the driver is overloaded and cannot fully manage the traffic situation. The human driver can also perform other productive or enjoyable activities during the journey, thus, reducing the opportunity cost of time spent in the car.

**Citation:** Magosi, Z.F.; Wellershaus, C.; Tihany, V.R.; Luley, P.; Eichberger A. Evaluation Methodology for Physical Radar Perception Sensor Models Based on On-Road Measurements for the Testing and Validation of Automated Driving. *Energies* **2022**, *15*, 2545. https:// doi.org/10.3390/en15072545

Academic Editors: Rui Esteves Araújo and Muhammad Aziz

Received: 31 January 2022 Accepted: 23 March 2022 Published: 31 March 2022

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Automated vehicles can significantly increase access and mobility for populations currently unable or not allowed to use conventional cars [4]. In addition, automated driving functions can improve fuel economy by providing more subtle acceleration and deceleration than a human driver. However, a number of issues remain to be resolved before self-driving vehicles become a reality.
