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

TL;DR

This paper investigates driver perceptions of integrated safety monitoring systems for alcohol impairment and distraction, addressing privacy, trust, comfort, and adoption. It reviews detection technologies across visual (eye-tracking), breath/biological sensors, and vehicle-motion cues, and examines system responses, effectiveness, and adoption costs, complemented by a US survey (n=115). Key findings show a preference for nonintrusive eye/posture monitoring over BrAC-based interlocks, strong privacy concerns with a preference for local data processing and anonymization, and a clear link between awareness, trust, and acceptance. The work provides design and policy guidance to balance safety benefits with personal autonomy, emphasizing transparency, reliability, and privacy safeguards to facilitate broader adoption of driver monitoring technologies.

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.

Figures (10)

• Figure 1: This graph compares the number of respondents that chose each false positive percentage as acceptable. Participants tended to favor lower percentages of false positives.
• Figure 2: This graph compares the number of respondents that chose each acceptable missed detection percentage. Participants tended to favor lower percentages of missed detections.
• Figure 3: This graph compares the average points for the Acceptance Score of each individual depending on the level of awareness. Those who have read or seen a lot of information on monitoring systems show a higher level of acceptance than those who have read or seen either some or no information on such systems.
• Figure 4: This graph compares the average acceptance score with education level; we observe no strong trend in the data.
• Figure 5: This graph compares the average acceptance score with income level; we observe no strong trend in the data.