On Expressing Automotive Maneuvers with SFC

Abstract

Conventional methods for testing autonomous driving software often involve dealing with a large number of dimensions, which can complicate the processing and analysis of test datasets. Therefore, there is a pressing need to develop a more efficient approach that is both time and cost-effective. In response to this challenge, our research aims to utilize space-filling curves to effectively represent possible maneuvers within high-dimensional autonomous driving data.

This study begins by conducting a comprehensive literature survey to explore the existing applications of spacefilling curves in the automotive domain. This investigation helps us gain insights into the current state of the field and understand how space-filling curves can be applied to address the complexities of autonomous driving. Additionally, we also review the literature concerning autonomous driving taxonomies to comprehend how existing taxonomies define various autonomous driving maneuvers, events, and scenarios.