General Behavioral Impact of Smart System Warnings: A Case of Advanced Driving Assistance Systems

• 成果类型: Article; Early Access
• 署名作者: Yang, Cenying; Agarwal, Ashish; Konana, Prabhudev
• 署名单位: City University of Hong Kong; University of Texas System; University of Texas Austin; University System of Maryland; University of Maryland College Park
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478251336742
• 发表日期: 2025
• 关键词: Smart Products; User Behavior; Automotive Telematics; Advanced Driving Assistance System and Warnings

摘要

Various sensors embedded in smart products can now provide alerts and warnings to users. However, these alerts can also influence user general behavior. We evaluate the effect of advanced driving assistance systems (ADAS) warnings on general driving behavior using automotive telematics data from a large automotive company. We categorize ADAS systems in terms of the response urgency and theorize that less urgent alerts lead to deliberate learning (System 2), improving driving behavior, while highly urgent warnings trigger automatic responses (System 1), potentially leading to risk compensation and worsening driving behavior. Our results show that the presence of a blind spot detection warning, which is less urgent with no immediate action, reduces the daily number of hard braking (speeding) events by 6.76% (9.34%). However, lane departure and forward collision warnings, which are highly urgent and require immediate responses, increase the daily number of hard braking (speeding) events by 5.65% (5.34%). Additionally, we find that, over time, the positive (negative) impact of the blind spot detection (lane departure and forward collision) feature improves (worsens). Finally, we quantify that the presence of the blind spot detection feature decreases the collision rate by 2.17% (3.14%) through a reduction in hard braking (speeding) incidents. However, the presence of lane departure and forward collision features reduces the safety benefits of preventing collisions by 1.71% (1.66%) due to an increase in hard braking (speeding) events. Our results call for the need to integrate user behavior into the design of smart features such as ADAS and related services.