Safety implications of providing real-time feedback to distracted drivers

doi:10.1016/j.aap.2006.10.003

Accident Analysis & Prevention

Volume 39, Issue 3, May 2007, Pages 581-590

https://doi.org/10.1016/j.aap.2006.10.003 Get rights and content

Abstract

A driving simulator study was conducted to assess whether real-time feedback on a driver's state can influence the driver's interaction with in-vehicle information systems (IVIS). Previous studies have shown that IVIS tasks can undermine driver safety by increasing driver distraction. Thus, mitigating driver distraction using a feedback mechanism appears promising. This study was designed to test real-time feedback that alerts drivers based on their off-road eye glances. Feedback was displayed in two display locations (vehicle-centered, and IVIS-centered) to 16 young and 13 middle-aged drivers. Distraction was observed as problematic for both age groups with delayed responses to a lead vehicle-braking event as indicated by delayed accelerator releases. Significant benefits were not observed for braking and steering behavior for this experiment, but there was a significant change in drivers’ interaction with IVIS. When given feedback on their distracted state, drivers looked at the in-vehicle display less frequently regardless of where feedback was displayed in the vehicle. This indicates that real-time feedback based on the driver state can positively alter driver's engagement in distracting activities, helping them attend better to the roadway.

Introduction

Driver distraction can interfere with the driving task, by sharing resources required for driving activities, such as visual, auditory, motor and cognitive resources (Ranney et al., 2000, Wierwille, 1993). A distracting activity generally encompasses more than one of these components. The introduction of in-vehicle information systems (IVIS), such as navigational displays, raises concerns because of the greater potential to distract the driver. The conflict between IVIS and driving-task demands is a source of driver distraction (Verwey, 2000) and needs to be mitigated.

Feedback can help an operator learn how a system or the environment is changing (Vakil and Hansman, 2002) and is especially important in complex environments and systems such as observed in aviation, nuclear power plants, and driving. The driving environment can change very rapidly and the driver may fail to track these changes, particularly if the driver's attention is captured by a non-driving related activity or if the driver is cognitively loaded (Haigney and Westerman, 2001, Horrey and Wickens, 2006, Lee et al., 2001). In such situations, feedback can help the driver adhere to environmental changes more appropriately. It is important to note that feedback should not increase the cognitive load of the driver because concurrent feedback can interfere with ongoing task performance (Arroyo et al., 2006, Corbett and Anderson, 2001).

Previous studies have evaluated the potential benefits of different driver distraction mitigation strategies to alert the drivers of roadway events (Donmez et al., in press, Donmez et al., 2006a). These studies presented a taxonomy of distraction mitigation strategies, of which two have been evaluated: an advising strategy that warned drivers of a safety-critical roadway event (i.e. a lead vehicle braking or an approaching curve), and a locking strategy that stopped drivers from engaging in an IVIS during these same events. The study showed that a locking strategy was beneficial in improving driving performance during engagements in visual distractions (Donmez et al., in press). However, a disadvantage of these aforementioned strategies is the inability to effectively warn drivers of prolonged engagement in the IVIS. Some distractions may degrade driving performance to safety critical levels even on straight roads with low levels of traffic. Moreover, high levels of distraction can be an indicator of a possible performance decrement. Providing feedback when the driver is highly distracted can help avoid future hazardous maneuvers.

Another disadvantage of mitigation strategies based only on the roadway state concerns non-useful alarms. Although there is a roadway event, such as a curve, the driver may actually be focused on the driving task and be able to respond quite appropriately. An alarm provided in this situation can degrade system acceptance and result in frustration, which itself is a distraction that can have a negative effect on traffic safety (Burns and Lansdown, 2000). This can be avoided by giving drivers feedback based on their attentional state rather than just the roadway state. Moreover, compared to warnings based on roadway events, which can affect immediate performance, feedback on drivers’ engagement in distractions can generate a long-term behavioral change.

Research investigating ways to direct a driver's attention to the roadway is mostly focused on providing drivers with alerts based on roadway events, such as forward collision (Lee et al., 2002) or lane departure warning systems (Suzuki and Jansson, 2003). Although the concept of adaptive automation has been previously investigated (Parasuraman and Hancock, 2001, Rouse, 1994), the application in the driving domain has been limited. Emerging technologies, such as non-intrusive eye tracking systems, make it possible to monitor the level of driver distraction and adjust feedback provided to the driver accordingly. Such systems may be able to monitor the vigilance levels of the driver (e.g. an alertometer) (Knipling, 1999). Other systems may be capable of filtering incoming phone calls when a real-time workload estimate exceeds some threshold value (Piechulla et al., 2003).

A system that monitors distraction would ideally generate a continuous indicator that identifies when the driver is too distracted to respond appropriately. This indicator could be used to warn drivers, or even to take over vehicle control. There are several variables that can be used to identify the distraction level while driving (e.g. heart rate variability, eye movements, and driving performance measures). However, the nature of the task will have an influence on the appropriate measure. For example, off-road glances would not be a good indicator of auditory or cognitive distractions, but are quite useful for visual distractions. Off-road glances have also been widely used in the evaluation of distractions for non-driving tasks (Hoffman et al., 2005, Sodhi et al., 2002), driver experience (Underwood et al., 2003), and driver fatigue (Ji et al., 2004).

The location of the feedback may also influence its effectiveness. Visual feedback embedded in IVIS can be more effective for mitigating IVIS distractions since that is where the drivers’ attention is centered. However, visual feedback presented elsewhere may mitigate distractions other than those associated with the IVIS. For example, if the driver is talking to a passenger or viewing a map, visual feedback that is embedded in an IVIS may be less effective. Thus, different distractions may require different feedback mechanisms. Alternatively, one feedback central to the vehicle can be implemented to avoid the potential information overload due to multiple feedbacks.

This study investigates whether real-time visual feedback regarding drivers’ off-road glances can alter drivers’ interactions with IVIS and enhance driving performance. It is hypothesized that the distraction associated with visual tasks will undermine driving performance and that real-time feedback based on eye movements will lead drivers to glance away from the road less frequently and for shorter periods. The more frequent sampling of the road can result in better timesharing between driving and the distracting task, and therefore enhance driving performance. Two locations are tested for feedback in this study: IVIS-centered, i.e., incorporated within the IVIS, and vehicle-centered, i.e., displayed on the dashboard. It is hypothesized that IVIS-centered feedback will be more effective than vehicle-centered feedback.

Section snippets

Participants

Equal numbers of participants were recruited in two age groups: young (18–21) and middle aged (35–55). There were 36 drivers in the study. However, due to the reliability of the eye tracker, seven participants’ data had to be omitted. The final analysis consisted of 29 participants: 16 young (M: 19.5; S.D.: 0.9, 7 male and 9 female) and 13 middle-aged drivers (M: 43.6; S.D.: 5.5, 7 male and 6 female). All participants possessed a valid U.S. driver's license, had at least one year of driving

Results

The mixed linear model (PROC MIXED statement in SAS 9.1) was used to analyze continuous dependent measures. This model was set up to account for unbalanced designs (e.g. different number of subjects) and repeated measures on subjects where the residuals are not independent. The mixed linear model also enables a variance-covariance matrix to be adequately incorporated for repeated measures. There are different types of variance covariance matrices (e.g. compound symmetry, unstructured, first

Discussion

Feedback based on drivers’ off-road eye glances, was evaluated in a driving simulator at two display locations (IVIS-centered and vehicle-centered). Distractions had detrimental effects on driving performance. Regardless of display location, the feedback altered driver's interaction with IVIS resulting in less frequent glances to the display and longer roadway glances. Surprisingly, there were no significant benefits in their braking or steering response, even though drivers looked at the

Acknowledgments

This research was conducted as part of the SAVE-IT program under contract by Delphi Corporation and sponsored by the U.S. DOT – National Highway Traffic Safety Administration – Office of Vehicle Safety Research. The authors acknowledge the technical support and comments provided by Mike Perel of NHTSA and Mary Stearns of the U.S. DOT—Volpe Center. Data collection for this project was also provided in part by Gregory Scott working under the Iowa Biosciences Advantage (IBA) program.

References (42)

P.J. Cooper
Differences in accident characteristics among elderly drivers and between elderly and middle-aged drivers
Acc. Anal. Prev.
(1990)
H.A. Deery
Hazard and risk perception among young novice drivers
J. Safety Res.
(1999)
B. Donmez et al.
Drivers’ attitudes towards imperfect distraction mitigation strategies
Trans. Res. F: Psychol. Behav.
(2006)
N.P. Gregersen
Young drivers’ overestimation of their own skill–an experiment on the relation between training strategy and skill
Acc. Anal. Prev.
(1996)
D.R. Mayhew et al.
Changes in collision rates among novice drivers during the first months of driving
Acc. Anal. Prev.
(2003)
M.M. Minderhoud et al.
Extended time-to-collision measures for road traffic safety assessment
Acc. Anal. Prev.
(2001)
W. Piechulla et al.
Reducing drivers’ mental workload by means of an adaptive man-machine interface
Transp. Res. F
(2003)
K. Suzuki et al.
An analysis of driver's steering behaviour during auditory or haptic warnings for the designing of lane departure warning system
JSAE Rev.
(2003)
S.S. Vakil et al.
Approaches to mitigating complexity-driven issues in commercial autoflight systems
Rel. Eng. Syst. Safety
(2002)
J. Van Der Laan et al.
A simple procedure for the assessment of acceptance of advanced transport telematics
Transp. Res. C
(1997)

K. Vogel

A comparison of headway and time to collision as safety indicators

Acc. Anal. Prev.

(2003)

A.F. Williams

Teenage drivers: patterns of risk

J. Safety Res.

(2003)

H. Akaike

A bayesian extension of the minimum AIC procedure of autoregressive model fitting

Biometrika

(1979)

J.J. Arnett

Developmental sources of crash risk in young drivers

Injury Prev.

(2002)

E. Arroyo et al.

CarCoach: a polite and effective driving coach

Burns, P.C., Lansdown, T.C., 2000. E-distraction: The Challenges for Safe and Usable Internet Services in Vehicles....

A.T. Corbett et al.

Locus of feedback control in computer-based tutoring: impact on learning rate, achievement, and attitudes

Donmez, B., Boyle, L., Lee, J.D., in press. The impact of distraction mitigation strategies on driving performance....

B. Donmez et al.

Assessing differences in young drivers’ engagement in distractions

D. Haigney et al.

Mobile (cellular) phone use and driving: a critical review of research methodology

Ergonomics

(2001)

J.D. Hoffman et al.

Visual sampling of in-vehicle text messages: the effects of number of lines, page presentation and message control

Transp. Res. Rec.

(2005)

Cited by (156)

Exploring the Behaviour Change Wheel and the Theoretical Domains Framework in interventions for mobile phone driver distraction: A scoping review
2024, Accident Analysis and Prevention
Mobile phone use while driving remains a significant traffic safety concern. Although numerous interventions have been developed to address it, there is a gap in the synthesis of relevant information through a comprehensive behaviour change lens. This scoping review uses the Behaviour Change Wheel (BCW) and the Theoretical Domains Framework (TDF) to examine the literature to (a) identify behavioural constructs targeted in interventions for mobile phone use while driving, (b) determine if the intervention success varied by sociodemographic group (e.g., age, gender, driving experience), and (c) map interventions to TDF domains to highlight areas for future research. Following the PRISMA extension for scoping reviews, we searched seven databases and identified 5,202 articles. After screening, 50 articles detailing 56 studies met the following inclusion criteria: (a) intervention studies, (b) providing details on methods and results, (c) written in English, and (d) targeting any driver behaviour related to mobile phone use while driving with a bottom-up approach, using not regulation or law enforcement, but individuals’ psychological processes, such as cognitive, behavioural, and emotional. Findings show that most interventions targeted young drivers and were typically effective. Except for a few studies, the effectiveness of interventions targeting different sociodemographic groups either remained untested or revealed nonsignificant differences. This finding points to a gap in the literature, indicating a need for further investigation into the efficacy of interventions for different groups, and for tailoring and testing them accordingly. The interventions also often targeted multiple TDF domains, complicating the interpretation of the relative efficacy of specific domains. Most frequently targeted domains included beliefs and consequences, emotions, knowledge, social influence, social/professional role and identity, and behavioural regulation. Physical skills and optimism domains were not targeted in any intervention. Further, almost all interventions addressed deliberate engagement in mobile phone distractions, while the automatic and fast processes involved in such behaviours were often overlooked. Mobile phone distractions are in part habitual behaviours, yet the existing mitigation efforts mostly assumed intentional engagement. More focus on the habitual nature of mobile phone distractions is needed.
Car-following crash risk analysis in a connected environment: A Bayesian non-stationary generalised extreme value model
2023, Analytic Methods in Accident Research
A connected environment provides driving aids to assist drivers in decision-making and aims to make driving manoeuvres safer by minimising uncertainty associated with decisions. The role of a connected environment becomes vital for car-following manoeuvres in a safety–critical event, whereby drivers follow a lead vehicle, and if timely action is not taken, it is likely to lead to a rear-end collision. Moreover, how different drivers perceive and react to the same information needs to be explored to understand the differential effects of a connected environment on car-following behaviour. As such, this study investigated the effects of the traditional and connected environments on car-following crash risk using traffic conflict techniques. Data were collected using the CARRS-Q advanced driving simulator, whereby 78 participants performed a car-following task in two randomised driving conditions: baseline (without driving aids) and connected environment (with driving aids). The safety–critical event in the car-following scenario was the leader’s hard braking, for which participants received advance information, besides several other driving aids. Modified time-to-collision was used as the traffic conflict measure for characterising rear-end crash risk and modelled using a generalised extreme value (GEV) model in the Bayesian framework. This model incorporated driving-related factors and driver demographics to address the non-stationarity issue of traffic extremes. Results reveal that the car-following crash risk is significantly reduced in the connected environment. Further, using the developed model, separate GEV distributions were estimated for each individual driver, providing insights into the heterogeneous effects of the connected environment on crash risk. The developed model was employed to understand the crash risk across different driver characteristics, and results suggest that crash risk decreases for all age groups and gender, with the maximum safety benefits obtained by young and female drivers. The findings of this study shed light on the efficacy of the connected environment in improving car-following behaviour and drivers’ ability to make safer decisions.
Impact of feedback content on dangerous driving behaviours based on the triangle model of responsibility
2023, Transportation Research Part F: Traffic Psychology and Behaviour
The serious problem of road traffic accidents is in urgent need of coping strategies, among which feedback has proven to be helpful. The main purpose of this study is to explore effective feedback content and the mediating effect of drivers’ responsibility based on the triangle model of responsibility. We designed a mixed study with 3 driving scenarios (getting too close to lead vehicle, speeding, and running a yellow light) and 4 feedbacks (no feedback, feedback with only a prescription, feedback with only an event, feedback with both prescription and event) as within-subject factors and 2 driver identities (feedback without driver identity, feedback with driver identity) as a between-subjects factor. A total of 110 Chinese drivers completed this study. The results showed that providing feedback enhanced drivers’ willingness to drive safely, while the effects of feedback with different contents varied with scenario. In addition, the mediating effect of drivers’ responsibility between feedback and speeding was confirmed. This study revealed that the effectiveness of feedback would be significantly influenced by a subtle change in feedback content in different driving scenarios, suggesting the importance of tailoring feedback content according to specific situations. Additionally, we provided a possible explanation of the mechanism of feedback by verifying the mediating effect of responsibility. Our findings of effective feedback contents corresponding to specific driving scenarios could be used to design driving feedback in safety warning systems to improve the effectiveness of such systems in facilitating safe driving behaviour and reducing road traffic accidents.
A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods
2022, Journal of Safety Research
Introduction: The existing selection of driving distraction recognition methods is based on a specific research perspective and does not provide comprehensive information on the entire field of view. Method: We conducted a systematic review of previous studies, aiming to come up with appropriate research methods to identify the driver's distraction state. First, this article selects four sets of search keywords related to driving distraction discrimination from five databases (Web of Science, ScienceDirect, Springer Link, IEEE, and TRID) and identifies 1,620 peer-reviewed documents from 2000 to 2020; these 1,620 documents underwent bibliographic analysis and co-occurrence network analysis. The co-occurrence coupling relationship is analyzed from the aspects of time, country, publication, author and keywords. Second, 37 papers published were screened, and the driving distraction recognition methods proposed by these 37 papers were summarized and analyzed. Results: The results show that this field has been prevalent since 2013; countries such as the United States, Britain, Germany, Australia, China, and Canada are in the forefront of research in this field, and the cooperation between related countries is relatively close. The cooperation between authors is characterized by aggregation, and the mobile phone as the main keyword is almost connected to other keyword nodes; the recognition model of deep learning algorithm based on video surveillance data sources has become the mainstream hot spot distraction recognition method. The recognition model of machine learning algorithm based on vehicle dynamics data, driver physiology, and eye movement data sources has specific advantages and disadvantages. Practical Applications: The results can help people to understand the current situation of driving distraction comprehensively and systematically, provide better theoretical support for researchers to choose the subsequent driving distraction recognition model, and provide research direction for driving distraction recognition in the future.
Analysis of distracted driving crashes in New Jersey using mixed logit model
2022, Journal of Safety Research
Introduction: Distracted driving is a concern for traffic safety in the 21st century, and can be held responsible for the increasing propensity and severity of traffic crashes. With the advent of mobile technologies, distractions involving the use of cellphones while driving have emerged, and young drivers in particular are getting more and more engaged in these distractions. Texting or receiving phone calls while driving are offenses in most states, and they are punished with fiscal penalties. Awareness campaigns have also been arranged over recent decades across the United States in order to minimize crashes due to distracted driving. The severity of such crashes depends on driver behavior, which can also be affected by various factors like the geometric design of the roadway, lighting and environmental conditions, and temporal variables. Method: In this study, we analyzed data on five years (2015–2019) of crashes involving cellphone use in New Jersey using a mixed logit model. As estimated model parameters can vary randomly across roadway segments in this approach, this allowed us to account for unobserved heterogeneities relating to roadway characteristics, environmental factors, and driver behavior. A pseudo-elasticity analysis was further employed to observe the sensitivity of the significant explanatory variables to crash severity. Results: We found that higher speed limits and a larger total number of vehicles involved both increased crash severity, while higher annual average daily traffic (AADT) levels and the presence of an urban road setting reduced it. Practical Applications: These findings will help decision-makers to comprehend what the significant contributing factors associated with crash injury severity due to distracted driving are, and how to implement necessary interventions to reduce this severity.
An Extreme Value Theory approach to estimate crash risk during mandatory lane-changing in a connected environment
2022, Analytic Methods in Accident Research
Examining crash risk in the highly anticipated connected environment is hindered by its novelty and the consequent scarcity of relevant data. This study proposes an Extreme Value Theory approach to examine and quantify mandatory lane-changing crash risk in the traditional and connected environments using traffic conflict techniques. The CARRS-Q advanced driving simulator was utilised to collect trajectory data of 78 participants performing mandatory lane-changing manoeuvres in three randomised driving conditions: baseline (without driving aids), connected environment with perfect communication, and connected environment with communication delay. Using the exceedance statistics theory (also known as a Peak Over Threshold approach corresponding to Generalised Pareto distribution), three separate models corresponding to each driving condition were developed. Driving-related factors obtained from the driving simulator data, such as speeds, spacings, lag gaps, and remaining distances, as well as driver demographics, were used as input variables to these models. Relative crash risk analysis and characteristics of the fitted Generalised Pareto distributions were employed as indicators of safety. The findings suggest that the connected environment significantly reduces mandatory lane-changing crash risk compared with the baseline condition, with the highest risk reduction observed in the perfect communication condition. While the crash risk of the communication delay condition is higher than that of the perfect communication condition, it is lower than the baseline condition. Furthermore, a comparison of the developed model to its counterpart (i.e., Block Maxima approach) showed the better performance of the adopted approach. The findings of this study provide insights into the positive impact of the connected environment on the safety of mandatory lane-changing manoeuvres as well as confirm the veracity of Peak Over Threshold models in estimating crash risk using traffic conflict data.

View all citing articles on Scopus

View full text

Safety implications of providing real-time feedback to distracted drivers

Abstract

Introduction

Section snippets

Participants

Results

Discussion

Acknowledgments

Acc. Anal. Prev.

J. Safety Res.

Trans. Res. F: Psychol. Behav.

Acc. Anal. Prev.

Acc. Anal. Prev.

Acc. Anal. Prev.

Transp. Res. F

JSAE Rev.

Rel. Eng. Syst. Safety

Transp. Res. C

Acc. Anal. Prev.

J. Safety Res.

A bayesian extension of the minimum AIC procedure of autoregressive model fitting

Biometrika

Developmental sources of crash risk in young drivers

Injury Prev.

CarCoach: a polite and effective driving coach

Locus of feedback control in computer-based tutoring: impact on learning rate, achievement, and attitudes

Assessing differences in young drivers’ engagement in distractions

Mobile (cellular) phone use and driving: a critical review of research methodology

Ergonomics

Visual sampling of in-vehicle text messages: the effects of number of lines, page presentation and message control

Transp. Res. Rec.