Patterns of Detecting Feature Interaction in Autonomous Car

Young Woo Lee, Heung Seok Chae

http://doi.org/10.5626/JOK.2019.46.10.1001

In a system with multiple features working together, unexpected behaviors may arise due to feature interaction. An interaction detection method that only considers the dependencies between a system"s components can cause false positives, because interactions are considered to occur between features that are not actually performed simultaneously. In addition, it does not take into account for the interactions that result from the association, such as speed and direction. This paper proposes a pattern with which to detect interaction based on time-series data of the system. In case studies, I classified interaction types by combining interaction attributes, mapped the patterns to identify each interaction, and then performed interaction detection based on the time series data of an autonomous car. For the ACC, OA, LKA, and EVA features of the autonomous car, interactions between speed and direction variables were detected using a non-continuous partitioning pattern and a repetitive partitioning pattern. The interaction resulting from the association between direction and speed variables was detected using a partition conflict pattern.