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The advent of Advanced Driver Assistance Systems (ADAS) and automated driving has offered a new challenge for functional verification and validation. The explosion of the test sample space for possible combinations of inputs needs to be handled in an intelligent manner to meet cost and time targets for the development of such systems. This paper addresses this research gap by using constrained randomization techniques for the creation of the required test scenarios and test cases. Furthermore, this paper proposes an automated constrained randomized test scenario generation framework for testing of ADAS and automated systems in a driving simulator setup. The constrained randomization approach is deployed at two levels: 1) test scenario randomization 2) test case randomization. The novelty of the proposed approach is in applying the constrained randomization method to generate test scenarios and test cases for automotive system and system of systems in a driving simulator environment.

A full vehicle NVH Simulator has been developed to provide a realistic interactive in-car environment where a subject can experience multi-modal stimuli ( accurately reproduced sound and vibration as well as visual ) whilst either driving or being driven. This paper describes its use in learning about the strategies subjects employ during sound evaluations, and how this information can help optimise decision making during product development. It is possible to understand how subjects assess the sound of vehicles, both in the way that they drive the vehicle and importantly which elements of the sound character have greatest influence on their evaluation of the vehicle. It is also possible to compare the strategies employed by NVH engineers, company decision-makers and non-experts such as customers.