Accurate calibration is essential for reliable perception in advanced driver assistance systems (ADAS), because sensor misalignment can degrade object detection, localization, and tracking. This study compares static and dynamic calibration of a forward-looking multifunction camera (MFK) and two front corner radar sensors, designated as nanoradars (NR) in the project documentation, installed on an MQB-based test vehicle. Static calibration was performed under controlled end-of-line conditions, whereas dynamic calibration was conducted during on-road operation. Camera yaw, pitch, and roll were evaluated in static, daytime, and nighttime scenarios; radar yaw was evaluated statically and during daytime dynamic trials at three locations. Because the road tests did not include an independent metrological ground truth, the analysis used deviation from the static reference, dispersion, repeatability, radar-pair descriptors, and utilization of project-specific acceptance limits rather than absolute pose accuracy. All results met the applicable criteria. The largest camera offset and dispersion occurred in roll at nighttime Location C. The radar pair exhibited persistent opposite-sign yaw estimates with unequal magnitudes and sensor-specific dynamic responses, while reflector configuration affected the location-dependent results. Dynamic calibration therefore provides an operationally representative but environment-dependent calibration state. Static calibration remains necessary as a controlled baseline, and dynamic calibration provides complementary in-use validation. Their combination supports more robust calibration strategies for automotive perception sensors.