Evaluating Driver Perceptions of Integrated Safety Monitoring Systems for Alcohol Impairment and Distraction

Patibandla, RoshikNagaSai; Greer, Ross · 2025 · arXiv

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Summary

This study investigates driver perceptions of integrated safety monitoring systems designed to detect alcohol impairment and distraction, motivated by the urgent need to reduce alcohol-related traffic fatalities and the impending regulatory mandate for such technologies in new vehicles. With alcohol-impaired driving causing significant economic and human costs, and the U.S. Infrastructure Investment and Jobs Act requiring passive impaired driving prevention systems by 2026–2027, the research aims to understand the trade-offs drivers accept between enhanced safety and personal autonomy. The authors seek to determine how factors like privacy, trust, and system intrusiveness influence public acceptance and willingness to adopt these monitoring tools. To evaluate these perceptions, the researchers conducted a survey of 115 U.S. participants, recruited through local networks and research labs. The survey assessed demographics, awareness of existing technologies, comfort levels with various monitoring methods (eye movement, posture, breath alcohol concentration), trust in system accuracy, privacy preferences, and willingness to pay for such features. The study also introduced an Individual Acceptance Score (IAS) to quantify overall acceptance based on responses regarding comfort, trust, and behavioral adaptation. Data was analyzed to identify correlations between acceptance scores and variables such as awareness, education, income, and vehicle automation levels. The results indicate that drivers prefer non-intrusive monitoring systems, such as eye-tracking, over restrictive technologies like ignition interlocks. Privacy emerged as a primary concern, with 76.52% of respondents preferring local data processing and 83.33% favoring identity anonymization. Trust in system accuracy was moderate (average score of 3.03 out of 5) but crucial for behavioral change; drivers who trusted the systems were more likely to adapt their driving in response to alerts. Notably, higher awareness of monitoring technologies correlated with greater acceptance, while education and income levels showed no significant impact. Drivers exhibited low tolerance for system errors, with many accepting only minimal false positives or missed detections, though this tolerance increased with higher trust levels. Additionally, drivers were more willing to accept monitoring if they were concerned about their own driving behavior, indicating a desire for personal accountability. The study concludes that widespread adoption of driver monitoring systems depends on addressing privacy concerns, ensuring high reliability, and increasing public awareness. Manufacturers and regulators must prioritize transparent data practices and local processing to build trust. The findings suggest that while there is foundational support for these systems, success requires balancing safety benefits with personal freedom. By improving transparency and educating the public, these technologies can effectively reduce road accidents without facing significant resistance from drivers who value their autonomy.

Key finding

Driver acceptance of integrated alcohol-and-distraction monitoring systems is driven primarily by awareness of the technology and trust in its accuracy rather than education or income; drivers prefer non-intrusive eye-movement monitoring, locally processed and anonymized data, over restrictive measures like BrAC ignition interlocks.

Methodology

survey

Sample size: N=115 U.S. adults

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via discover_arxiv on 2026-05-04 (4 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• alcohol detection systems
• dui enforcement
• dms validation
• alcohol
• situational awareness
• distraction detection algorithms

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: observational prevalence
• Methodological Resource: validation psychometrics, tool software