SafetyLens: Visual Data Analysis of Functional Safety of Vehicles

TL;DR

SafetyLens is a visual data analysis tool designed to help automotive engineers analyze complex functional safety datasets, improving safety assessment processes for modern vehicles with electronic systems.

Contribution

The paper introduces SafetyLens, a novel visual analysis tool tailored for automotive functional safety data, developed through a design study with domain experts.

Findings

Positive user feedback from domain experts

Effective visualization techniques for safety data analysis

Enhanced understanding of safety dataset patterns

Abstract

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.