Analyzing Drivers’ Distractions due to Smartphone Usage: Evidence from AutoLog Dataset

Abstract

The usage of a smartphone while driving has been declared a global portent and has been admitted as a leading cause of crashes and accidents. Numerous solutions, such as Android Auto and CarPlay, are used to facilitate for the drivers by minimizing driver distractions. However, these solutions restrict smartphone usage, which is impractical in real driving scenarios. This research paper presents a comprehensive analysis of the available solutions to identify issues in smartphone activities. We have used empirical evaluation and dataset-based evaluation to investigate the issues in the existing smartphone user interfaces. The results show that using smartphones while driving can disrupt normal driving and may lead to change the steering wheel abruptly, focus off the road, and increases cognitive load, which could collectively result in a devastating situation. To justify the arguments, we have conducted an empirical study by collecting data using maxed mode survey, i.e., questionnaires and interviews from 98 drivers. The results show that existing smartphone-based solutions are least suitable due to numerous issues (e.g., complex and rich interfaces, redundant and time-consuming activities, requiring much visual and mental attention, and contextual constraints), making their effectiveness less viable for the drivers. Based on findings obtained from Ordinal Logistic Regression (OLR) models, it is recommended that the interactions between the drivers and smartphone could be minimized by developing context-aware adaptive user interfaces to overcome the chances of accidents.

Different types of mobile phone use while driving and influencing factors on intention and behavior: Insights from an expanded theory of planned behavior

“…It is well-established that driver distraction, especially from cell phone use, contributes significantly to crashes [4,10]. Research indicates that the use of smartphones while driving disrupts normal driving behavior, resulting in sudden steering changes, reduced concentration, and increased cognitive load [11]. Numerous studies have investigated the impact of MPUWD on driving performance, including reaction time, speed, lane positioning, and collision risk [12][13][14].…”

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network

“…Sensors would also have two types of priority-level data packets, one being the usual data detected and the other being emergency or life-critical data [ 20 – 22 ]. High priority, less delay, and error-free data transmission under specified time limits [ 23 , 24 ] are needed for life-critical data. The working theory of sensor networks is focused on event-driven strategy and refers to many-to-one interaction [ 25 – 27 ].…”

Smartphone-Based Cognitive Assistance of Blind People in Room Recognition and Awareness

“…Besides the availability of technology-assisted solutions for navigation and localization, it is of immense importance to find the number of people, understand their social status, age, and gender, and navigate to and find the intended audience/person to communicate their message. For this, we use a smartphone because it is a better off-the-shelf gadget available with a large number of sensors [18][19][20][21]. ese sensors are responsible for sensing the ambient environment and positioning of the smartphone [22,23].…”