A Survey of Driver Distraction and Inattention in Popular Commercial Software-Defined Vehicles
November 04, 2025 ยท The Cartographer ยท ๐ arXiv.org
"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Survey of Driver Distraction and Inattention in Popular Commercial Software-Defined Vehicles"
Evidence collected by the PWNC Scanner
Authors
Lingyu Zhao, Yuankai He
arXiv ID
2511.02891
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.SE
Citations
0
Venue
arXiv.org
Last Checked
5 days ago
Abstract
As the automotive industry embraces software-defined vehicles (SDVs), the role of user interface (UI) design in ensuring driver safety has become increasingly significant. In crashes related to distracted driving, over 90% did not involve cellphone use but were related to UI controls. However, many of the existing UI SDV implementations do not consider Drive Distraction and Inattention (DDI), which is reflected in many popular commercial vehicles. This paper investigates the impact of UI designs on driver distraction and inattention within the context of SDVs. Through a survey of popular commercial vehicles, we identify UI features that potentially increase cognitive load and evaluate design strategies to mitigate these risks. This survey highlights the need for UI designs that balance advanced software functionalities with driver-cognitive ergonomics. Findings aim to provide valuable guidance to researchers and OEMs to contribute to the field of automotive UI, contributing to the broader discussion on enhancing vehicular safety in the software-centric automotive era.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Human-Computer Interaction
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
๐ป
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
๐ป
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
๐ป
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
๐ป
Ghosted