ArticleSN computer science2023
How Older Drivers Perceive Warning Alerts? Insights for the Design of Driver-Car Interaction.
Article in SN computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
The automotive industry is working toward driving automation and driver-assistance technology is becoming a norm in modern cars. Warning alert systems support the driver-car interaction and inform drivers about automation system status, upcoming obstacles, or dangers ahead. However, older drivers' needs are not always addressed in research studies, although they make up a large segment of drivers. Therefore, we conducted a qualitative three-round formative evaluation of a warning alert system using video prototypes in lab and remote settings. The goal was to evaluate visual-, sound-, and speech-based alerts based on: (a) their efficiency in informing drivers about the road situation ahead, and (b) participants' subjective opinions. We evaluated the system's efficiency using self-reported data measuring participants' cognitive load, usability, UX, and ease of use. Also, we conducted interviews to collect subjective feedback about proposed prototypes. In this article, we describe the design of warning alerts and report on their evaluation results. Our results show that speech-based warnings, especially when coupled with visual warnings, are efficient and accepted well by the participants. This article illustrates older drivers' attitude toward the use of different warning modalities in the driving context.
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