Automotive industry is gaining momentum with advanced technologies. Numerous companies are experimenting to make their vehicles safe and better. Advanced Driver Assistance Systems (ADAS) have become increasingly common in modern vehicles. It is known for providing crucial safety features to drivers. It uses automotive radar technology to scan roads, detecting obstacles and make split-second decisions. All these are related to ensuring safety on the road.

It is important to note here that rigorous testing is required for the radar technology to perform reliably. Automotive radar end-of-line (EOL) testing serves as the final checkpoint at the manufacturing end before installing in the vehicles. The testing phase is of course critical as it ensures that the radar sensors are functioning accurately.

One key aspect of radar testing is the Field of View (FOV) test. It simply helps in verifying that radar sensors to detect objects accurately on the road. The test is conducted using specialized automated radar test systems and it includes a radar target simulator (RTS) within an anechoic chamber. It allows simulation of various objects like pedestrians or cars at different distances and angles.

Lots of data is collected from the radar sensors and test systems during the testing phase. The data is thereafter analyzed with the use of big data analytics to provide actionable insights and enhance quality control. Analytics software processes the data to generate high-level views of critical metrics. It tries to find out the track production targets in real-time as well as predict potential issues based on historical trends.

Suppose the software detects a decline in radar measurement quality over time, the anomalies are flagged and it indicates a potential issue within the testing system. The issue can be temperature variations affecting radar simulation accuracy.