Work

Assessing Driver’s Trust, Compliance, and Reliance in an Automated Flood Warning System: Effects of Error Types and System Reliability

Year: 2024

Type: article

Abstract: The increasing threat of inland flooding due to precipitation changes and floodplain development necessitates efficient real-time flood detection and communication methods. While automated floodwarning systems facilitate such communication, they are susceptible to errors like false alarms and misses, which could undermine drivers’ trust during flood events. This study examined how system accuracy and error type impact perceived system reliability, as well as drivers’ trust and behaviors. Our results showed that both false alarms and misses lowered drivers’ perceived system reliability, and drivers were more inclined to follow recommendations from a system with higher reliability compared to one with low reliability. Misses and false alarms influenced drivers’ reliance and compliance behaviors differently. These findings help predict how system reliability level and error type shape drivers’ responses to automated flood-warning systems, potentially contributing to their design and calibration.

Source: Proceedings of the Human Factors and Ergonomics Society Annual Meeting

DOI: 10.1177/10711813241276493

Authors: Tianyi Mao, Katherine R. Garcia, Jing Chen

Institution: Rice University

Language: English

Topic: Human-Automation Interaction and Safety

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,200

Funder: National Science Foundation