Validation is one of the main challenges in development of automated driving systems. Due to the complexity of these systems and the various influence factors on their functional safety, current testcase generation methods can hardly guarantee the completeness of the validation on system level. Separate validation of system components according to the requirements from system is a way to make system approval possible. In this paper, an approach is presented to generate deductively testcases for the validation of the environment perception sensors, one of the most essential components of automated driving systems. This approach starts from the model-based testing method, which is commonly used to validate software-based systems, and is extended by considering various external influence factors as follows: By modeling and analyzing each application in automated driving systems, the application oriented usecases of perception sensors are first derived. With the aid of a classification of perception sensor errors, the sensor error types that are critical for each usecase are identified. Meanwhile, based on sensor working principle, the correspondence between external influence factors and each type of sensor errors is summarized in a morphological box. By combining the external influence factors, which can “stimulate” a certain sensor error type that is critical for usecases, testcases can be generated. Adaptive Cruise Control (ACC) is analyzed exemplary in this paper as an application in level 3 system instead of a level 1 function in reality. This paper presents a structured deduction of testcases, which makes a complete validation of perception sensor for an automated driving application possible. Additionally, we also show some possibilities of testcase reduction. An outlook for further development and utilization is presented as well.