scholarly journals What are Australian drivers doing behind the wheel? An overview of secondary task data from the Australian Naturalistic Driving Study

2019 ◽  
Vol 30 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Kristie Young ◽  
Rachel Osborne ◽  
Sjaan Koppel ◽  
Judith Charlton ◽  
Raphael Grzebieta ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Secondary Task ◽  
Task Engagement ◽  
Driver Distraction ◽  
Secondary Tasks ◽  
Mobile Phone Use ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Using Data

Using data from the Australian Naturalistic Driving Study (ANDS), this study examined patterns of secondary task engagement (e.g., mobile phone use, manipulating centre stack controls) during everyday driving trips to determine the type and duration of secondary task engaged in. Safety-related incidents associated with secondary task engagement were also examined. Results revealed that driver engagement in secondary tasks was frequent, with drivers engaging in one or more secondary tasks every 96 seconds, on average. However, drivers were more likely to initiate engagement in secondary tasks when the vehicle was stationary, suggesting that drivers do self-regulate the timing of task engagement to a certain degree. There was also evidence that drivers modified their engagement in a way suggestive of limiting their exposure to risk by engaging in some secondary tasks for shorter periods when the vehicle was moving compared to when it was stationary. Despite this, almost six percent of secondary tasks events were associated with a safety-related incident. The findings will be useful in targeting distraction countermeasures and policies and determining the effectiveness of these in managing driver distraction.

Crash and Near-Crash Risk Assessment of Distracted Driving and Engagement in Secondary Tasks: A Naturalistic Driving Study

2018 ◽  
Vol 2672 (38) ◽  
pp. 245-254 ◽  
Author(s):  
Peter R. Bakhit ◽  
BeiBei Guo ◽  
Sherif Ishak
Keyword(s):  
Traffic Safety ◽  
Real World ◽  
Secondary Task ◽  
Safety Concern ◽  
Distracted Driving ◽  
Crash Risk ◽  
Secondary Tasks ◽  
Depth Analysis ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.

Systematic Review of Research on Driver Distraction in the Context of Advanced Driver Assistance Systems

2021 ◽  
pp. 036119812110041
Author(s):  
Apoorva P. Hungund ◽  
Ganesh Pai ◽  
Anuj K. Pradhan
Keyword(s):  
Systematic Review ◽  
Task Performance ◽  
Secondary Task ◽  
Task Engagement ◽  
Driver Distraction ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Secondary Tasks ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Advanced driver assistance systems (ADAS) promise improved driving performance and safety. With ADAS taking on more vehicle control tasks, the driver’s role may be reduced to that of passive supervision. This in turn may increase drivers’ engagement in non-driving-related tasks, thereby potentially reducing any promised safety benefit. We conducted a systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to study the relationship between ADAS use and driver distraction. Four research questions were addressed—two questions examined the effect of ADAS on secondary task engagement, and the quality of secondary task performance, and two addressed the effects of ADAS on driver attention and on driver behavior changes caused by secondary task engagement. Twenty-nine papers were selected for full text synthesis. The majority of the papers indicate an association between ADAS and increased secondary task engagement, as well as improved secondary task performance. Ten papers reported that drivers tend to divert their attention to secondary tasks and away from driving tasks. These outcomes highlight the continued importance of the role of the human driver despite vehicle automation, especially in the context of driver distraction, and that user understanding of ADAS functionalities and limitations is essential to appropriate and effective use of these systems.

scholarly journals Modelling the Effect of Mobile Phone Use on Driving Behaviour Considering Different Use Modes

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Ioanna Spyropoulou ◽  
Maria Linardou
Keyword(s):  
Reaction Time ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Lateral Position ◽  
Driving Performance ◽  
Driver Distraction ◽  
Accident Risk ◽  
Mobile Phone Use ◽  
Driving Behaviour ◽  
Driving Speed

Mobile phone use while driving is a major cause of driver distraction, affecting driving performance and increasing accident risk. Governments have responded to this with the implementation of legislation prohibiting the use of mobile phones, under specific conditions, mainly adopting the hands-free use. Still, mobile phone is a cause of several types of distraction rather than just manual. This study explores the effect of mobile phone use while driving via a simulator experiment. Participants drive under various types of mobile phone use mode- namely, handheld, hands-free (wired earphone), and speaker to capture this effect. Results highlight the effect of mobile phone use, regardless of the use mode, on driving behaviour through specific indicators: maximum driving speed, reaction time, and lateral position. In particular, considering the aforementioned parameters the handheld mode demonstrates safer driving behaviour compared to the speaker mode. The results of this study stress the need for a reconsideration of the present legislation.

scholarly journals Naturalistic Driving Study in Brazil: An Analysis of Mobile Phone Use Behavior while Driving

2020 ◽  
Vol 17 (17) ◽  
pp. 6412 ◽  
Author(s):  
Jorge Tiago Bastos ◽  
Pedro Augusto B. dos Santos ◽  
Eduardo Cesar Amancio ◽  
Tatiana Maria C. Gadda ◽  
José Aurélio Ramalho ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Average Frequency ◽  
The United States ◽  
International Studies ◽  
Middle Income ◽  
Mobile Phone Use ◽  
Trip Time ◽  
European Values ◽  
Naturalistic Driving ◽  
Use Behavior

Mobile phone use (MPU) while driving is an important road safety challenge worldwide. Naturalistic driving studies (NDS) emerged as one of the most sophisticated methodologies to investigate driver behavior; however, NDS have not been implemented in low- or middle-income countries. The aim of this research is to investigate MPU while driving and compare the results to those reported in international studies. An analysis of 61.32 h and 1350 km driven in Curitiba (Brazil) showed that MPU lasted for an average of 28.51 s (n = 627) and occurred in 58.71% of trips (n = 201) with an average frequency of 8.37 interactions per hour (n = 201). The proportion of the trip time using a mobile phone was 7.03% (n = 201), and the average instantaneous speed was 12.77 km/h (n = 627) while using the phone. Generally, drivers spent less time on more complex interactions and selected a lower speed when using the phone. MPU was observed more during short duration than longer trips. Drivers in this study engaged in a larger number of MPU compared to drivers from Netherlands and the United States; and the percentage of trip time with MPU was between North American and European values.

Influences of Gesture-Based Mobile Phone Use While Driving

2021 ◽  
pp. 036119812110263
Author(s):  
Jianwei Niu ◽  
Yulin Zhou ◽  
Dan Wang ◽  
Xingguo Liu
Keyword(s):  
Mobile Phone ◽  
Lateral Position ◽  
Driving Safety ◽  
Control Technique ◽  
Steering Wheel ◽  
Secondary Tasks ◽  
Mobile Phone Use ◽  
Driving Speed ◽  
Gesture Control ◽  
The Impact

The use of mobile phones while driving has been a hot topic in the field of driving safety for decades. Although there are few studies on the influence of gesture control on in-vehicle secondary tasks, this study aims to investigate the impact of gesture-based mobile phone use without touching while driving from the perspective of multiple-resource workload owing to visual, auditory, cognitive, and psychomotor resource occupation. A novel gesture control technique was adopted for secondary task interactions, to recognize the gestures of drivers. An experiment was conducted to study the influences of two interaction modes, traditional touch-based mobile phone interaction and gesture-based mobile phone interaction, on driving behavior in three different cognitive level task groups. The results indicate that gesture-based mobile phone interaction can improve driving performance with regard to lateral position-keeping ability and steering wheel control; nevertheless, it has no significant impact on longitudinal metrics such as driving speed, driving speed variation, and throttle control variation. Gesture-based mobile phone interactions have a larger effect on secondary tasks with medium cognitive load but not on actual operation tasks. It was also verified that the performance of gesture-based mobile phone interaction was better in secondary mobile phone tasks such as switching (e.g., switching songs) and adjusting (e.g., adjusting volume) than the traditional interaction mode. This study provides the theoretical and experimental support for human–computer interaction using gesture-based mobile phone interactive control in future automobiles.

Accounting for Driver Distraction and Socioeconomic Characteristics in a Crash Risk Index: Naturalistic Driving Study

2017 ◽  
Vol 2659 (1) ◽  
pp. 204-211 ◽  
Author(s):  
Mengqiu Ye ◽  
Osama A. Osman ◽  
Sherif Ishak
Keyword(s):  
Risk Index ◽  
The United States ◽  
Distracted Driving ◽  
Crash Risk ◽  
Grading System ◽  
Secondary Tasks ◽  
Auto Insurance ◽  
Socioeconomic Characteristics ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving has long been acknowledged as one of the main contributors to crashes in the United States. According to past studies, driving behavior proved to be influenced by the socioeconomic characteristics of drivers. However, few studies attempted to quantify that influence. This study proposed a crash risk index (CRI) to estimate the crash risk associated with the socioeconomic characteristics of drivers and their tendency to experience distracted driving. The analysis was conducted with data from the SHRP 2 Naturalistic Driving Study. The proposed CRI was developed on a grading system of three measures: the crash risk associated with performing secondary tasks during driving, the effect of socioeconomic attributes (e.g., age) on the likelihood of engagement in secondary tasks, and the effect of specific categories within each socioeconomic attribute (e.g., age older than 60) on the likelihood of engagement in secondary tasks. Logistic regression analysis was performed on the secondary tasks, socioeconomic attributes, and specific socioeconomic characteristics. The results identified the significant secondary tasks with high crash risk and the socioeconomic characteristics with significant effect on determining drivers’ involvement in secondary tasks in each tested parameter. These results were used to quantify the grading system measures and hence estimate the proposed CRI. This index indicates the relative crash risk associated with the socioeconomic characteristics of drivers and considers the possibility of engagement in secondary tasks. The proposed CRI and the associated grading system are plausible methods for estimating auto insurance premiums.

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