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Bai J, Sun X, Cao S, Wang Q, Wu J. Exploring the Timing of Disengagement From Nondriving Related Tasks in Scheduled Takeovers With Pre-Alerts: An Analysis of Takeover-Related Measures. HUMAN FACTORS 2024:187208231226052. [PMID: 38207243 DOI: 10.1177/00187208231226052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
OBJECTIVES This study aimed to investigate drivers' disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance. BACKGROUND During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks. METHOD Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives. RESULTS The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3. CONCLUSION Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two. APPLICATION Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.
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Affiliation(s)
| | - Xu Sun
- University of Nottingham Ningbo, China
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, China
| | - Shi Cao
- University of Waterloo, Canada
| | - Qingfeng Wang
- Nottingham University Business School China, University of Nottingham, China
| | - Jiang Wu
- University of Nottingham Ningbo, China
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Cuentas-Hernandez S, Li X, King MJ, Oviedo-Trespalacios O. The impact of road traffic context on secondary task engagement while driving. Front Psychol 2023; 14:1139373. [PMID: 37077849 PMCID: PMC10108847 DOI: 10.3389/fpsyg.2023.1139373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 02/14/2023] [Indexed: 04/05/2023] Open
Abstract
Introduction Driver distraction has been recognized for a long time as a significant road safety issue. It has been consistently reported that drivers spend considerable time engaged in activities that are secondary to the driving task. The temporary diversion of attention from safety-critical driving tasks has often been associated with various adverse driving outcomes, from minor driving errors to serious motor vehicle crashes. This study explores the role of the driving context on a driver's decision to engage in secondary activities non-critical to the driving task. Method The study utilises the Naturalistic Engagement in Secondary Tasks (NEST) dataset, a complementary dataset derived from the SHRP2 naturalistic dataset, the most extensive naturalistic study to date. An initial exploratory analysis is conducted to identify patterns of secondary task engagements in relation to context variables. Maximum likelihood Chi-square tests were applied to test for differences in engagement between types of driver distraction for the selected contextual variables. Pearson residual graphs were employed as a supplementary method to visually depict the residuals that constitute the chi-square statistic.Lastly, a two-step cluster analysis was conducted to identify common execution scenarios among secondary tasks. Results The exploratory analysis revealed interesting behavioral trends among drivers, with higher engagement rates in left curves compared to right curves, while driving uphill compared to driving downhill, in low-density traffic scenarios compared to high-density traffic scenarios, and during afternoon periods compared to morning periods. Significant differences in engagement were found among secondary tasks in relation to locality, speed, and roadway design. The clustering analysis showed no significant associations between driving scenarios of similar characteristics and the type of secondary activity executed. Discussion Overall, the findings confirm that the road traffic environment can influence how car drivers engage in distracted driving behavior.
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Affiliation(s)
- Sandra Cuentas-Hernandez
- QUT Faculty of Health, School of Psychology and Counselling, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Xiaomeng Li
- QUT Faculty of Health, School of Psychology and Counselling, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Mark J. King
- QUT Faculty of Health, School of Psychology and Counselling, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Oscar Oviedo-Trespalacios
- QUT Faculty of Health, School of Psychology and Counselling, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Delft Faculty of Technology, Policy and Management, Department of Values, Technology and Innovation, Delft University of Technology, Delft, Netherlands
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Peng Y, Song G, Guo M, Wu L, Yu L. Investigating the impact of environmental and temporal features on mobile phone distracted driving behavior using phone use data. ACCIDENT; ANALYSIS AND PREVENTION 2023; 180:106925. [PMID: 36512902 DOI: 10.1016/j.aap.2022.106925] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/22/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Mobile phone distracted driving (MPDD) is one of the most significant and common factors in distraction-affected crashes. In previous studies, MPDD has been described as a self-selected behavior that affects driving performance, rather than a multidimensionally impacted behavior. In this study, the researchers hypothesized that external environmental features significantly impacted MPDD and tested this hypothesis by structural equation modeling (SEM). Three external latent variables (road, operation, and control factors) were measured at different times during weekdays in urban areas of Texas by integrating a large number of mobile phone sensor data and roadway inventory data. A structural model was developed to test the relationship between the latent variables and the rate of drivers involved in MPDD (MPDDR) on the roadway during different time periods. Finally, the data summary and model results revealed significant temporal effects. Standardized estimates from the SEM results revealed the positive impact of roads factors in the morning peak that broader shoulders, wider medians, and smaller curve radians were correlated with higher MPDDR in the morning peak hours; the negative impact of operation factors that higher average annual daily truck traffic (truck AADT) were associated with lower MPDDR significantly. And the impact of control factors on MPDDR is positive. In other words, the road segments with a large number of traffic signals in urban areas had a higher MPDDR than those without traffic signals. These findings could assist transportation and legislation agencies in the development of appropriate countermeasures or enforcement tactics and implement them effectively to reduce the occurrence of MPDD. In addition, this study provides a novel perspective close to the actual consideration of drivers about using mobile phones while driving, in the context of MPDD research, rather than comparing driver groups and vehicle performance.
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Affiliation(s)
- Yongxin Peng
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuancun, Haidian District, Beijing 100044, China.
| | - Guohua Song
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuancun, Haidian District, Beijing 100044, China.
| | - Manze Guo
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuancun, Haidian District, Beijing 100044, China.
| | - Lingtao Wu
- Center for Transportation Safety, Texas A&M Transportation Institute, College Station, TX 77843-3135, United States.
| | - Lei Yu
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuancun, Haidian District, Beijing 100044, China.
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Luo S, Yi X, Shao Y, Xu J. Effects of Distracting Behaviors on Driving Workload and Driving Performance in a City Scenario. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15191. [PMID: 36429906 PMCID: PMC9690507 DOI: 10.3390/ijerph192215191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Distractors faced by drivers grow continuously, and concentration on driving becomes increasingly difficult, which has detrimental influences on road traffic safety. The present study aims to investigate changes in driving workload and driving performance caused by distracting tasks. The recruited subjects were requested to drive along a city route in a real vehicle and perform three secondary tasks sequentially. Electrocardiography and driving performance were measured. Heart rate variability (HRV) was adopted to quantitatively analyze the driving workload. Findings show that: (i) increments are noticed in the root mean square differences of successive heartbeat intervals (RMSSD), the standard deviation of normal-to-normal peak (SDNN), the heart rate growth rate (HRGR), and the ratio of low-frequency to high-frequency powers (LF/HF) compared to undistracted driving; (ii) the hands-free phone conversation task has the most negative impacts on driving workload; (iii) vehicle speed reduces due to secondary tasks while changes in longitudinal acceleration exhibit inconsistency; (iv) the experienced drivers markedly decelerate during hands-free phone conversation, and HRGR shows significant differences in both driving experience and gender under distracted driving conditions; (v) correlations exist between HRV and driving performance, and LF/HF correlates positively with SDNN/RMSSD in the hands-free phone conversation and chatting conditions while driving.
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Affiliation(s)
- Shuang Luo
- College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
| | - Xinxin Yi
- Chongqing Chang’an Automobile Co., Ltd., Chongqing 400023, China
| | - Yiming Shao
- College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
| | - Jin Xu
- College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
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Li Q, Hou L, Wang Z, Wang W, Zeng C, Yuan Q, Cheng B. Drivers' visual-distracted take-over performance model and its application on adaptive adjustment of time budget. ACCIDENT; ANALYSIS AND PREVENTION 2021; 154:106099. [PMID: 33770718 DOI: 10.1016/j.aap.2021.106099] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/15/2021] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
There are certain situations that automated driving (AD) systems are still unable to handle, preventing the implementation of Level 5 AD. Thus, a transition of control, colloquially known as take-over of the vehicle, is required when the system sends a take-over request (TOR) upon exiting the operational design domain (ODD). An adaptive TOR along with good take-over performance requires adjusting the time budget (TB) to drivers' visual distraction state, adhering to a reliable visual-distraction-based take-over performance model. Based on a number of driving simulator experiments, the percentage of face orientation to distraction area (PFODA) and time to boundary at take-over timing (TTBT) were proposed to accurately evaluate the degree of visual distraction based on merely face orientation under naturalistic non-driving related tasks (NDRTs) and to evaluate take-over performance, respectively. In order to elucidate the safety boundary, this study also proposed an algorithm to set a suitable minimum value of the TTBT. Finally, a multiple regression model was built to describe the relationship among PFODA, TB and TTBT along with a corrected coefficient of determination of 0.748. Based on the model, this study proposed an adaptive TB adjustment method for the take-over system.
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Affiliation(s)
- Qingkun Li
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China; Center for Intelligent Connected Vehicles and Transportation, Tsinghua University, Beijing, 100084, China.
| | - Lian Hou
- Institute of Industrial Science, The University of Tokyo, Tokyo, 1538505, Japan.
| | - Zhenyuan Wang
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
| | - Wenjun Wang
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China; Center for Intelligent Connected Vehicles and Transportation, Tsinghua University, Beijing, 100084, China.
| | - Chao Zeng
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China.
| | - Quan Yuan
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China; Center for Intelligent Connected Vehicles and Transportation, Tsinghua University, Beijing, 100084, China.
| | - Bo Cheng
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
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Drivers' Attention Strategies before Eyes-off-Road in Different Traffic Scenarios: Adaptation and Anticipation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18073716. [PMID: 33918239 PMCID: PMC8038146 DOI: 10.3390/ijerph18073716] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/05/2022]
Abstract
The distribution of drivers’ visual attention prior to diverting focus from the driving task is critical for safety. The object of this study is to investigate drivers’ attention strategy before they occlude their vision for different durations under different driving scenarios. A total of 3 (scenarios) × 3 (durations) within-subjects design was applied. Twenty-three participants completed three durations of occlusion (0, 1, and 2 s) test drive in a motion-based driving simulator under three scenarios (urban, rural, motorway). Drivers’ occlusion behaviour, driving behaviour, and visual behaviour in 6 s before occlusion was analyzed and compared. The results showed that drivers tended to slow down and increased their attention on driving task to keep safety in occlusion 2 s condition. The distribution of attention differed among different driving scenarios and occlusion durations. More attention was directed to Forward position and Speedometer in occlusion conditions, and a strong shift in attention from Forward position to Road users and Speedometer was found in occlusion 2 s condition. Road users was glanced more frequently in urban road with a higher percentage of attention transitions from Forward position to Road users. While gaze switching to Speedometer with a higher intensity was found on motorway. It suggests that drivers could adapt their visual attention to driving demand and anticipate the development of upcoming situations by sampling enough driving-related information before eyes-off-road. Moreover, the adaptation and anticipation are in accordance with driving situation and expected eyes-off-road duration. Better knowledge about attentional strategies before attention away from road contributes to more efficient and safe interaction with additional tasks.
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Reinmueller K, Kiesel A, Steinhauser M. Adverse Behavioral Adaptation to Adaptive Forward Collision Warning Systems: An Investigation of Primary and Secondary Task Performance. ACCIDENT; ANALYSIS AND PREVENTION 2020; 146:105718. [PMID: 32847736 DOI: 10.1016/j.aap.2020.105718] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 01/31/2020] [Accepted: 08/03/2020] [Indexed: 06/11/2023]
Abstract
Advanced driver assistance systems can effectively support drivers but can also induce unwanted effects in behavior. The present study investigates this adverse behavioral adaptation in adaptive Forward Collision Warning (FCW) systems. Other than conventional FCW systems that provide warnings based on static Time-To-Collision (TTC) thresholds, adaptive FCW systems consider the driver's need for support by adjusting warning thresholds according to distraction. A neglected question is how drivers adapt their behavior when they use adaptive FCW systems under realistic conditions, i.e., when warnings occur infrequently but system functionality is anticipated. Forty-eight participants drove with two different FCW systems (adaptive vs. non-adaptive) while working on a secondary in-vehicle task in a driving simulator. During the main part of the experiment, no brake events occurred and hence FCW functioning was largely anticipated. Additionally, visual system feedback about the driver's distraction state was manipulated between groups. Participants had significantly shorter minimal time-headways and TTCs when driving with the adaptive relative to the non-adaptive system. Participants with system feedback about distraction state spent generally more time with engaging in the secondary task. These results indicate behavioral adaptation which, however, is restricted to the task that is specifically supported by the system, namely longitudinal control.
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Affiliation(s)
| | - Andrea Kiesel
- Department of Psychology, Albert-Ludwigs-University of Freiburg, Engelbergerstraße 41, D-79085 Freiburg, Germany
| | - Marco Steinhauser
- Department of Psychology, Catholic University of Eichstätt-Ingolstadt, Ostenstraße 25, D-85072 Eichstätt, Germany
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Wandtner B, Schömig N, Schmidt G. Effects of Non-Driving Related Task Modalities on Takeover Performance in Highly Automated Driving. HUMAN FACTORS 2018; 60:870-881. [PMID: 29617161 DOI: 10.1177/0018720818768199] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
OBJECTIVE Aim of the study was to evaluate the impact of different non-driving related tasks (NDR tasks) on takeover performance in highly automated driving. BACKGROUND During highly automated driving, it is allowed to engage in NDR tasks temporarily. However, drivers must be able to take over control when reaching a system limit. There is evidence that the type of NDR task has an impact on takeover performance, but little is known about the specific task characteristics that account for performance decrements. METHOD Thirty participants drove in a simulator using a highly automated driving system. Each participant faced five critical takeover situations. Based on assumptions of Wickens's multiple resource theory, stimulus and response modalities of a prototypical NDR task were systematically manipulated. Additionally, in one experimental group, the task was locked out simultaneously with the takeover request. RESULTS Task modalities had significant effects on several measures of takeover performance. A visual-manual texting task degraded performance the most, particularly when performed handheld. In contrast, takeover performance with an auditory-vocal task was comparable to a baseline without any task. Task lockout was associated with faster hands-on-wheel times but not altered brake response times. CONCLUSION Results showed that NDR task modalities are relevant factors for takeover performance. An NDR task lockout was highly accepted by the drivers and showed moderate benefits for the first takeover reaction. APPLICATION Knowledge about the impact of NDR task characteristics is an enabler for adaptive takeover concepts. In addition, it might help regulators to make decisions on allowed NDR tasks during automated driving.
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Wandtner B, Schumacher M, Schmidt EA. The role of self-regulation in the context of driver distraction: A simulator study. TRAFFIC INJURY PREVENTION 2016; 17:472-479. [PMID: 27082493 DOI: 10.1080/15389588.2015.1102231] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 09/28/2015] [Indexed: 06/05/2023]
Abstract
OBJECTIVE There is considerable evidence for the negative effects of driver distraction on road safety. In many experimental studies, drivers have been primarily viewed as passive receivers of distraction. Thus, there is a lack of research on the mediating role of their self-regulatory behavior. The aim of the current study was to compare drivers' performance when engaged in a system-paced secondary task with a self-paced version of this task and how both differed from baseline driving performance without distraction. METHODS Thirty-nine participants drove in a simulator while performing a secondary visual-manual task. One group of drivers had to work on this task in predefined situations under time pressure, whereas the other group was free to decide when to work on the secondary task (self-regulation group). Drivers' performance (e.g., lateral and longitudinal control, brake reaction times) was also compared with a baseline condition without any secondary task. RESULTS For the system-paced secondary task, distraction was associated with high decrements in driving performance (especially in keeping the lateral position). No effects were found for the number of collisions, probably because of the lower driving speeds while distracted (compensatory behavior). For the self-regulation group, only small impairments in driving performance were found. Drivers engaged less in the secondary task during foreseeable demanding or critical driving situations. CONCLUSIONS Overall, drivers in the self-regulation group were able to anticipate the demands of different traffic situations and to adapt their engagement in the secondary task, so that only small impairments in driving performance occurred. Because in real traffic drivers are mostly free to decide when to engage in secondary tasks, it can be concluded that self-regulation should be considered in driver distraction research to ensure ecological validity.
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Affiliation(s)
- Bernhard Wandtner
- a Federal Highway Research Institute (BASt) , Bergisch Gladbach , Germany
| | - Markus Schumacher
- a Federal Highway Research Institute (BASt) , Bergisch Gladbach , Germany
| | - Eike A Schmidt
- a Federal Highway Research Institute (BASt) , Bergisch Gladbach , Germany
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McNabb J, Gray R. Staying Connected on the Road: A Comparison of Different Types of Smart Phone Use in a Driving Simulator. PLoS One 2016; 11:e0148555. [PMID: 26886099 PMCID: PMC4757568 DOI: 10.1371/journal.pone.0148555] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 01/19/2016] [Indexed: 12/02/2022] Open
Abstract
Previous research on smart phone use while driving has primarily focused on phone calls and texting. Drivers are now increasingly using their phone for other activities during driving, in particular social media, which have different cognitive demands. The present study compared the effects of four different smart phone tasks on car-following performance in a driving simulator. Phone tasks were chosen that vary across two factors: interaction medium (text vs image) and task pacing (self-paced vs experimenter-paced) and were as follows: Text messaging with the experimenter (text/other-paced), reading Facebook posts (text/self-paced), exchanging photos with the experimenter via Snapchat (image, experimenter -paced), and viewing updates on Instagram (image, experimenter -paced). Drivers also performed a driving only baseline. Brake reaction times (BRTs) were significantly greater in the text-based conditions (Mean = 1.16 s) as compared to both the image-based conditions (Mean = 0.92 s) and the baseline (0.88 s). There was no significant difference between BRTs in the image-based and baseline conditions and there was no significant effect of task-pacing. Similar results were obtained for Time Headway variability. These results are consistent with the picture superiority effect found in memory research and suggest that image-based interfaces could provide safer ways to “stay connected” while driving than text-based interfaces.
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Affiliation(s)
- Jaimie McNabb
- Human Systems Engineering, Arizona State University, Mesa, Arizona, United States of America
| | - Rob Gray
- Human Systems Engineering, Arizona State University, Mesa, Arizona, United States of America
- * E-mail:
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