Application Oriented Testcase Generation for Validation of Environment Perception Sensor in Automated Driving Systems

Validation is one of the main challenges in development of automated driving systems (ADS). 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 and effectivity of the validation on system level. Separate validation of system components 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, which are the most essential components of ADS. This approach is originated from the model-based testing method, which is commonly used to validate software-based systems and extended by considering various external influence factors as follows: By modeling and analyzing applications in ADS, application oriented usecases of perception sensors are first derived. Based on 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 sensor error type are summarized in a morphological box. By combining the factors, which can “stimulate” a certain sensor error type that is critical for usecases, testcases can be generated. As an example, adaptive cruise control (ACC) system is analyzed as application in level 3 system instead of level 1 function in reality. This paper presents a structured deduction of testcases to make a complete validation of perception sensor for ADS possible. Some possibilities of testcase reduction and an outlook for further development and utilization are presented as well.