Immersive Analysis of Spatiotemporal Racing Data
Nicole Weidinger, Neven ElSayed, Granit Luzhnica, Tobias Schreck, Eduardo Enrique Veas · 2023
This paper presents an exploratory study of expert behavior while analysing spatio-temporal data in an immersive environment for the racing engineering domain. The analysis of moving-object data has become crucial in diverse fields, including sports analysis, logistics and autonomous navigation. This data provides valuable insights into events occurring at specific points in space and time, enabling professionals to make informed decisions. For example, in motorsports, race engineers rely on spatio-temporal data and vehicle telemetry to identify key events and optimize performance. However, conventional stacked line charts used by race engineers offer limited spatial association. Effective visualization techniques that incorporate the spatial context are essential to fully understand and interpret the complex nature of moving-object data. To address this, we propose novel immersive designs for analyzing motorsports data. Our study uses a critical task in racing engineering—comparing cornering performance of two drivers to document patterns of behavior of expert analysis. Experts used conventional charts, situated 3D graphs and a hybrid with both options within an Immersive Analytics environment. Results demonstrate the potential of our approach. All experts preferred the hybrid visualization combining conventional and situated approaches. In the short study period, experts found value in the situated approach to understand differences in distances between events.