SafetyLens: Visual Data Analysis of Functional Safety of Vehicles
This addresses the problem of safety analysis for engineers in the automotive industry, but it is incremental as it applies existing visualization techniques to a specific domain.
The paper tackles the challenge of analyzing automotive Functional Safety datasets by introducing SafetyLens, a visual data analysis tool that assists engineers in minimizing risks through network exploration and visual comparison, resulting in design guidelines and positive user feedback.
Modern automobiles have evolved from just being mechanical machines to having full-fledged electronics systems that enhance vehicle dynamics and driver experience. However, these complex hardware and software systems, if not properly designed, can experience failures that can compromise the safety of the vehicle, its occupants, and the surrounding environment. For example, a system to activate the brakes to avoid a collision saves lives when it functions properly, but could lead to tragic outcomes if the brakes were applied in a way that's inconsistent with the design. Broadly speaking, the analysis performed to minimize such risks falls into a systems engineering domain called Functional Safety. In this paper, we present SafetyLens, a visual data analysis tool to assist engineers and analysts in analyzing automotive Functional Safety datasets. SafetyLens combines techniques including network exploration and visual comparison to help analysts perform domain-specific tasks. This paper presents the design study with domain experts that resulted in the design guidelines, the tool, and user feedback.