A Scoping Study on Driver’s Perspective of Distracting Factors

Distracting activities while driving are common and can result in errors that threaten road users’ safety. The main objectives of this study were to investigate drivers’ perspectives of the factors contributing to distraction, determine the relative rank of types of distractions, recognize the road factors and environmental effects that make distractions more dangerous, and identify the most effective measures to reduce driver distractions. A survey was conducted to assess Jordanian drivers’ experiences with distracted driving, and what solutions they believed could be implemented to solve the problems. The study’s outcomes revealed that drivers perceive visual distractions as the most dangerous, followed by cognitive, manual, and auditory distractions, respectively. It was also found that “mobile phone texting or dialing” was ranked the top most dangerous visual and manual distracting factor. “Baby is crying or kids are fighting in the back seat” was perceived by all demographic groups as the riskiest auditory factor. Regarding cognitive distraction, four factors were perceived as the most serious, of which “Baby is crying”, “Driving while angry or sad or agitated”, “Talking on a cell phone—even a hands-free one” and “Conversing with passengers” were determined to be the top four distracting factors. The results also revealed that drivers believe that “laws and enforcement” is the most effective measure to reduce distractions while driving.

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

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.

Technology Helps Combat Driver Fatigue: Case Study From the Middle East

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203350, “How Technology Can Support Tackling Drivers’ Fatigue: Case From Petroleum Development Oman,” by Demir Hadzic and Hamed Al Esry, Petroleum Development Oman, and David Marsh, Sheida International, prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually 9–12 November. The paper has not been peer reviewed. Petroleum Development Oman operates in harsh environments over which their drivers cover more than 320 million km annually. Driver fatigue is one of the leading causes of motor vehicle incidents (MVIs) associated with operations. The objective of the complete paper is to understand further how technology can support the prevention of driver fatigue and to explore driver beliefs related to fatigue and the technology designed to assist in fatigue avoidance. This study helped the operator’s safety specialists understand driver fatigue and develop mechanisms to prevent it. Introduction Previous research has found that motor vehicle crash fatalities in the oil and gas industry are up to 8.5 times more com-mon than in other occupations. A qualitative study by the operator indicated that participants commonly perceived fatigue as a main factor in MVIs. However, understanding of the nature and role of fatigue in MVIs was revealed to be relatively limited. Among the driver group interviewed, a common view existed that the responsibility for managing driver fatigue lay with the employer and not with drivers. In addition, among the driver group, there appeared to be little understanding of the effects that lifestyle outside of work has on driver fatigue. The pilot study showed that driver fatigue occurred once every three journeys on average, whereas driver distractions could occur more than four times in a single journey. Equipment and Processes Technology used in the study consists of in-cabin hardware units, analytical software integration with in-vehicle monitoring systems (IVMS), and participation of a monitoring team. In addition, event verification is performed by human monitors and feedback. Driver fatigue is part of a comprehensive integrated journey-management system developed by the operator, including standard operating procedures with regard to monitoring and event classification, verification, and response.

Implementing a Gaze Tracking Algorithm for Improving Advanced Driver Assistance Systems

Car accidents are one of the top ten causes of death and are produced mainly by driver distractions. ADAS (Advanced Driver Assistance Systems) can warn the driver of dangerous scenarios, improving road safety, and reducing the number of traffic accidents. However, having a system that is continuously sounding alarms can be overwhelming or confusing or both, and can be counterproductive. Using the driver’s attention to build an efficient ADAS is the main contribution of this work. To obtain this “attention value” the use of a Gaze tracking is proposed. Driver’s gaze direction is a crucial factor in understanding fatal distractions, as well as discerning when it is necessary to warn the driver about risks on the road. In this paper, a real-time gaze tracking system is proposed as part of the development of an ADAS that obtains and communicates the driver’s gaze information. The developed ADAS uses gaze information to determine if the drivers are looking to the road with their full attention. This work gives a step ahead in the ADAS based on the driver, building an ADAS that warns the driver only in case of distraction. The gaze tracking system was implemented as a model-based system using a Kinect v2.0 sensor and was adjusted on a set-up environment and tested on a suitable-features driving simulation environment. The average obtained results are promising, having hit ratios between 96.37% and 81.84%.

Modern approaches for detection of driver distraction

In this paper the author reviews various methods for detecting driver distractions. The considered methods are structured and classifi ed according to such characteristics as the possibility of implementation in the car, used sources of data, used algorithms, and the possibility of applying them on the fl y. Distraction detection allows to prevent traffi c accidents, and the collected statistics can be used as one of the factors for calculation of driver ratings.

Role of Habits in Cell Phone-Related Driver Distractions

Despite increased media attention and legislation banning some forms of cell phone use while driving, drivers continue to engage in illegal cell phone distractions. Several studies have used the theory of planned behavior (TPB) to explain why drivers voluntarily engage in cell phone distractions, and found that TPB constructs (attitudes, social norms, perceived behavioral control) predict intentions to engage in cell phone distractions while driving. Given that cell phone use is ubiquitous, habits that have formed around their general use may lead to automatic engagement in cell phone distractions while driving. This differs from voluntary engagement, in that habits are carried out automatically, with little thought given to the action or its consequences. Thus, in addition to the TPB constructs that explain intentions, habitual factors should also be considered in understanding why drivers use cell phones. A few studies have examined the role of habits in this context, but they only focused on texting behaviors. An online survey was conducted with 227 respondents to investigate the role of habitual cell phone use in driver engagement in a variety of illegal cell phone tasks (e.g., social media, email). Habitual cell phone use was found to explain unique variance in self-reported engagement after controlling for TPB constructs. Overall, the findings indicate that cell-phone-related distractions may not be entirely voluntary; instead, cell phone habits developed outside of the driving context appear to have a significant effect, suggesting that cell phone use while driving may have become automatic to a certain extent.

Towards the Design of Context-Aware Adaptive User Interfaces to Minimize Drivers’ Distractions

The usage of a smartphone while driving is a pervasive problem and has been acknowledged as a significant source of road accidents and crashes. Several solutions have been developed to control and minimize risky driving behavior. However, these solutions were mainly designed from the perspective of normal users to be used in a nondriving scenario. In a driving scenario, any deviation from these assumptions (e.g., touching or taping interfaces and looking to visual items) could impact driving performance. In this research paper, we aimed to design and develop a context-aware adaptive user interface framework to minimize driver distraction. The proposed framework is implemented in Android platform, namely, “DriverSense,” which is capable of adapting smartphone user interfaces based on contextual factors including driver preferences, environmental factors, and device usage in real time using adaptation rules. The proposed solution is evaluated both in real time using AutoLog application and through an empirical study by collecting data from 93 drivers through a mixed-mode survey using a questionnaire. Results obtained from AutoLog dataset show that performing activities on smartphone native interfaces while driving leads to abrupt changes in speed and steering wheel angle. However, minimal variations have been observed while performing activities on DriverSense interfaces. The results obtained from the empirical study show that the data are found to be internally consistent with 0.7 Cronbach’s alpha value. Furthermore, an Iterated Principal Factor Analysis (IPFA) retained 60 of a total of 61 measurement items with lower uniqueness values. The findings show that the proposed solution has significantly minimized the driver distractions and has positive perceptions in terms of usefulness, attitude, learnability and understandability, and user satisfaction.

Validation of Simulated Safety Indicators with Traffic Crash Data

The purpose of this document is to validate a new methodology useful for the estimation of road accidents resulting from possible driver distractions. This was possible through a statistical comparison made between real accident data between 2016 and 2018 in the city of Santander (Spain) and simulated data resulting from the application of the methodology on two areas of study. The methodology allows us to evaluate possible collisions starting from the knowledge of vehicular trajectories extrapolated from microsimulation. Studies show that there are good correlations between the real data and the simulated data. The results obtained show that the proposed methodology can be considered reliable and, therefore, it could be of fundamental importance for designers, since it would simplify the choice between different possible intervention scenarios, determining which is the least risky in terms of road safety.