Evaluating Driver Perceptions of Integrated Safety Monitoring Systems for Alcohol Impairment and Distraction
May 29, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
RoshikNagaSai Patibandla, Ross Greer
arXiv ID
2505.22969
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
The increasing number of accidents caused by alcohol-impaired driving has prompted the development of integrated safety systems in vehicles to monitor driver behavior and prevent crashes. This paper explores how drivers perceive these systems, focusing on their comfort, trust, privacy concerns, and willingness to adopt the technology. Through a survey of 115 U.S. participants, the study reveals a preference for non-intrusive systems, such as those monitoring eye movements, over more restrictive technologies like alcohol detection devices. Privacy emerged as a major concern, with many participants preferring local data processing and anonymity. Trust in these systems was crucial for acceptance, as drivers are more likely to adapt their behavior when they believe the system is accurate and reliable. To encourage adoption, it is important to address concerns about privacy and balance the benefits of safety with personal freedom. By improving transparency, ensuring reliability, and increasing public awareness, these systems could play a significant role in reducing road accidents and improving safety.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted