MetaSem: metamorphic testing based on semantic information of autonomous driving scenes

Abstract

The development of artificial intelligence and information communication technology has significantly propelled advancements in autonomous driving. The advent of autonomous driving has a profound impact on societal development and transportation methods. However, as intelligent systems, autonomous driving systems (ADSs) often make wrong judgements in specific scenarios, resulting in accidents. There is an urgent need for comprehensive testing and validation of ADSs. Metamorphic testing (MT) techniques have demonstrated effectiveness in testing ADSs. Nevertheless, existing testing methods primarily encompass relatively simple metamorphic relations (MRs) that only verify ADSs from a single perspective. To ensure the safety of ADSs, it is essential to consider the various elements of driving scenarios during the testing process. Therefore, this paper proposes MetaSem, a novel metamorphic testing method based on semantic information of autonomous driving scenes. Based on semantic information of the autonomous driving scenes and traffic regulations, we design 11 MRs targeting different scenario elements. Three transformation modules are developed to execute addition, deletion and replacement operations on various scene elements within the images. Finally, corresponding evaluation metrics are defined based on MRs. MetaSem automatically discovers inconsistent behaviours according to the evaluation metrics. Our empirical study on three advanced and popular autonomous driving models demonstrates that MetaSem not only efficiently generates visually natural and realistic scene images but also detects 11,787 inconsistent behaviours on three driving models.