Testing vision-based advanced driver assistance systems (ADAS) in a Camera-in-the-Loop (CiL) bench setup, where external visual inputs are used to stimulate the system, provides an opportunity to experiment with a wide variety of test scenarios, different types of vehicle actors, vulnerable road users, and weather conditions that may be difficult to replicate in the real world. In addition, once the CiL bench is setup and operating, experiments can be performed in less time when compared to track testing alternatives. In order to better quantify normal operating zones, track testing results were used to identify behavior corridors via a statistical methodology. After determining normal operational variability via track testing of baseline stationary surrogate vehicle and pedestrian scenarios, these operating zones were applied to screen-based testing in a CiL test setup to determine particularly challenging scenarios which might benefit from replication in a track testing environment. For this work, a Mobileye 6 aftermarket ADAS camera sensor system was tested in first a track environment and then a CiL simulation environment using Siemens Prescan. A variety of actor and environmental variables were tested, including different pedestrian and vehicle surrogates and varying levels of precipitation and atmospheric effects. From these tests, interesting scenarios in which there was a delayed or non-reaction from the camera were identified. These scenarios, in which the Mobileye system had variable performance, could be further tested in a track environment to better understand the response.