Privacy Concern and Acceptability of Driver Monitoring System
Authors: Chu Yueying , Yuan Zihui , Liu Peng 
Published: October 2023

Driver monitoring systems (DMS) are designed to track drivers’ attention status, accumulate real-time data, and intervene when symptoms of fatigue or distraction are observed, thereby enhancing driving safety [1]. In vehicles equipped with partial driving automation [2], the driver’s role necessitates constant attention to road conditions and monitoring of dynamic driving task (DDT). These vehicles usually feature an advanced driver-assistance system (ADAS), but due to limited understanding of ADAS functionalities, drivers might develop an overreliance on these systems. This could potentially lead to misuse or distractions [3]. Consequently, DMS assists in preventing traffic accidents by supporting drivers in their responsibilities, which includes providing responses in instances of driver negligence.The effectiveness of a DMS is directly related to the user’s willingness to share data such as facial images and vehicle behavior. Moreover, it’s often seen that privacy concerns inversely affect the readiness of users to disclose personal information [4]. Unlike technological domain, users’ perspective regarding DMS utilization has been under-explored. This study aims to gather insights into Chinese drivers’ attitudes towards DMS privacy issues and their willingness to adopt this technology. These findings will help evaluate the future prospects of DMS implementation. To facilitate this goal, the existing DMS systems are categorized into four types based on their primary features: facial image-based DMS, electroencephalogram signals-based DMS, electrocardiogram signals-based DMS, and vehicle behavior-based DMS.A one-way between-subjects design was conducted to investigate the influence of various DMS types on psychological perception and behavioral intention using an online survey (N = 486). Each participant was randomly assigned to one of four DMS type conditions. The questionnaire commenced with a succinct introduction to the relevant DMS type, including its name, functions, and methods of data collection. Subsequently, participants were asked to express their agreement or disagreement with 19 items across seven dimensions (data sensitivity, collection concern, secondary use, perceived insecurity, perceived usefulness, trust, and behavioral intention) about the involved DMS on a Likert scale ranging from 1 (totally disagree) to 7 (totally agree). The questionnaire ended up with demographic questions.All demographic variables did not differ significantly among different DMS type conditions. An exploratory factor analysis was conducted on the 19 items, revealing that three factors emerged, named “privacy concern,” “general acceptance,” and “data sensitivity.” Privacy concern is composed of the predetermined factor collection concern, secondary use, and perceived insecurity; general acceptance is composed of perceived usefulness, trust, and behavioral intention. Subsequently, we examined if DMS types influenced participants’ ratings on privacy concern, general acceptance, and data sensitivity. These analyses yielded no significant effects for DMS types on privacy concern and data sensitivity. Regarding general acceptance, participants displayed a positive attitude and significantly preferred vehicle behavior-based DMS. Further, we investigated the effect of DMS types on predetermined factors. The results showed that there was no significant effect for DMS types on collection concern, secondary use, and perceived insecurity. Participants believed that vehicle behavior-based DMS was more useful and trustworthy. Regression analysis indicated that data sensitivity was a positive explanatory variable for general acceptance, however, the privacy concern was a negative one. This study examined data sensitivity, privacy concern, and general acceptance of various DMS among drivers, and explored the factors influencing general acceptance. It was observed that Chinese drivers, in general, hold a favorable view of DMS and express a degree of willingness to use them. They are less worried about privacy and data insecurity. Further exploration is necessary to ascertain the readiness to use DMS in real-world scenarios.References:1. Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: A review. IEEE Trans. Intell. Transport. Syst. 12(2), 596–614 (2011)2. SAE International: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Society of Automotive Engineering, USA (2021)3. de Winter, J. C. F., Petermeijer, S. M., Abbink, D. A.: Shared control versus traded control in driving: A debate around automation pitfalls. Ergonomics (in press) 1–43 (2022)4. Hoffman, D., Novak, T. P., & Peralta, M.: Building consumer trust online. Commun. ACM. 42(4), 80–85 (1999)