Autonomous cars are developing in a rapid speed, which means that the advanced driver-assistance systems must be tested carefully. Radar is one of many tools the car can use to detect obstacles and targets in front of the car. This bachelor thesis deals with programming and testing of an FMCW radar. The radar used in this thesis is an Continental ARS 308-2C/21 HS/AO, and the programming is written in Visual Basic. The goal within this project was to write a program for the Continental radar, and then measure static objects. The radar and the written program will be used in the research project, "HiFi Radar Target". The radar module will also be implemented in the new EMC chamber, AWITAR, which is currently under construction. The radar sweeps in the frequency band 77GHz, which is a standard frequency in the automotive industry. Communication with the computer is done via a CAN-network. Function tests have been performed on Astazero and gave satisfactory results.The thesis work has been carried out at RISE in Borås. / Idag är självkörande bilar på kraftig frammarsch, detta medför att hjälpsystemen måste testas noga. Radar är ett av många hjälpmedel bilen kan använda för att detektera hinder framför bilen. Detta examensarbete avhandlar programering och testning av en FMCW radar. Radarn som används är en Continental ARS 308-2C/21 HS/AO, programeringen är skriven i Visual Basic. Målet med detta examensarbeta var att få radarn att fungera, för att sedan kunna mäta på statinära objekt. Radarn och det skrivna programmet kommer att användas inom forskningsprojektet, "HiFi Radar Target". Radarmodulen kommer även att implementeras i den nya EMC-kammaren, AWITAR som just nu är under konstruktion. Radarn sweper i freqvensbandet 77GHz, vilket är en standardfreqvens för bilindustrin. All kommunikation med radarn och datorn sker via ett CAN-nätverk. Funktionstesterna har utförts på Astazero och gav tillfredställande resultat. Examensarbetet är utfört på RISE i Borås.

In recent years, there have been remarkable advancements in automated driving systems. Consumer protection organizations, such as Euro NCAP, play a pivotal role in enhancing the overall safety of these modern vehicles. While previous emphasis has been on passive safety, the significance of active safety systems has surged in recent years. Evaluating the performance of these systems now relies on standardized test scenarios designed to simulate real-world accidents. Addressing this challenge, the future necessitates the incorporation of virtual methods to supplement traditional track tests. Given the complex nature of high-dimensional test cases, an exhaustive grid search is exceedingly time-consuming. In light of this challenge, we present a novel testing method utilizing search-based testing with Bayesian Optimization to efficiently navigate and explore the expansive search space of Euro NCAP CCR scenarios to identify the performance-critical scenarios. The methodology incorporates the Brake Threat Number as a robust criticality metric within the fitness function, providing a reliable indicator for assessing the inevitability of collisions. Furthermore, the research utilizes a surrogate model derived from the evaluation points used by the optimization algorithm to determine the performance-critical boundary that separates the critical and the non-critical scenarios. Additionally, this approach leverages the surrogate model for conducting sensitivity analysis, explaining the impact of individual parameters on the system’s output.