The Influence of the Secondary Tasks on Driving Performance and Mental Workload at Different Speeds

2019 ◽  
Author(s):  
Pei Chen ◽  
Qing Xue ◽  
Xiaobei Jiang
Keyword(s):  
Mental Workload ◽  
Driving Performance ◽  
Secondary Tasks

Effect of Secondary Tasks on Police Officer Cognitive Workload and Performance Under Normal and Pursuit Driving Situations

2021 ◽  
pp. 001872082110109
Author(s):  
Maryam Zahabi ◽  
Vanessa Nasr ◽  
Ashiq Mohammed Abdul Razak ◽  
Ben Patranella ◽  
Logan McCanless ◽  
...  
Keyword(s):  
Police Officer ◽  
Police Officers ◽  
Motor Vehicle ◽  
Mental Workload ◽  
Driving Performance ◽  
Cognitive Workload ◽  
Secondary Tasks ◽  
And Performance ◽  
Vehicle Technologies ◽  
Task Modality

Objective The objective of this study was to assess the effects of single and multiple secondary tasks on officers’ performance and cognitive workload under normal and pursuit driving conditions. Background Motor vehicle crashes are a leading cause of police line of duty injuries and deaths. These crashes are mainly attributed to the use of in-vehicle technologies and multi-tasking while driving. Method Eighteen police officers participated in a driving simulation experiment. The experiment followed a within-subject design and assessed the effect of single or multiple secondary tasks (via the mobile computer terminal (MCT) and radio) and driving condition (normal vs. pursuit driving) on officers’ driving performance, cognitive workload, and secondary task accuracy and reaction time. Results Findings suggested that police officers are protective of their driving performance when performing secondary tasks. However, their workload and driving performance degraded in pursuit conditions as compared to normal driving situations. Officers experienced higher workload when they were engaged with secondary tasks irrespective of the task modality or type. However, they were faster but less accurate in responding to the radio as compared to the MCT. Conclusion Police officers experience high mental workload in pursuit driving situations, which can reduce their driving performance and accuracy when they are engaged in some secondary tasks. Application The findings might be helpful for police agencies, trainers, and vehicle technology manufacturers to modify the existing policies, training protocols, and design of police in-vehicle technologies in order to improve police officer safety.

Introducing Multimodal Sliding Index: Qualitative Feedback, Perceived Workload, and Driving Performance with an Auditory Enhanced Menu Navigation Method

2017 ◽  
Author(s):  
Ruta R. Sardesai ◽  
Thomas M. Gable ◽  
Bruce N. Walker
Keyword(s):  
Mobile Device ◽  
Mental Workload ◽  
Driving Performance ◽  
Audio Cues ◽  
Qualitative Feedback ◽  
Speed Up ◽  
Driving Task ◽  
Perceived Workload ◽  
New Type ◽  
Navigation Method

Using auditory menus on a mobile device has been studied in depth with standard flicking, as well as wheeling and tapping interactions. Here, we introduce and evaluate a new type of interaction with auditory menus, intended to speed up movement through a list. This multimodal “sliding index” was compared to use of the standard flicking interaction on a phone, while the user was also engaged in a driving task. The sliding index was found to require less mental workload than flicking. What’s more, the way participants used the sliding index technique modulated their preferences, including their reactions to the presence of audio cues. Follow-on work should study how sliding index use evolves with practice.

Investigating the Effects of Visuospatial Memory Secondary Tasks on LCT Driving Performance

2017 ◽  
Vol 31 (10) ◽  
pp. 1759020 ◽  
Author(s):  
Ling Wu ◽  
Yueqi Hu ◽  
Tong Zhu ◽  
Haoxue Liu
Keyword(s):  
Working Memory ◽  
Memory Task ◽  
Driving Performance ◽  
Visuospatial Memory ◽  
Lane Change ◽  
Secondary Tasks ◽  
Perceived Workload ◽  
Memory Group ◽  
Lane Change Test ◽  
Lane Keeping

Memory demand is associated with increased mental workload. The objective of the present study was to examine the effects of visuospatial memory secondary tasks on driving performance. Memory tasks for the unknown word-figure pairs and recognition tasks for word-figure pairs at two-level difficulties were employed separately to represent working memory’s process and long-term memory’s process. A simulator study was conducted based on the simulation of the standard environment of Lane change test (LCT). The performance of lane keeping, lane change, and secondary tasks was measured by statistical methods. The comprehensive appraisal model was constructed to quantify total driving performance. The results showed that the mean path deviation, steering angle, and lane excursion times increased, and the proportion of correct lane change decreased, with the perceived workload increasing and the total driving performance decreasing in dual-task driving condition. Compared with the simple working memory group, as the difficulty of tasks increased in difficult working memory group, lane change performance degraded and the perceived workload increased. In contrast to difficult working memory group, the performance of lane keeping and lane change increased, while the perceived workload decreased and the total performance increased by about 50% in difficult recognition group. There were few differences between the simple working memory group and simple recognition group. The difficult working memory group had the lowest total driving performance. The results indicate that as the secondary task’s difficulty increases, driving performance will degrade. Performance improves significantly when the working memory process is converted to the recognition process. This trend is more obvious when the memory task assumes to be more difficult.

Driving While Interacting With Google Glass: Investigating the Combined Effect of Head-Up Display and Hands-Free Input on Driving Safety and Multitask Performance

2017 ◽  
Vol 59 (4) ◽  
pp. 671-688 ◽  
Author(s):  
Kathryn G. Tippey ◽  
Elayaraj Sivaraj ◽  
Thomas K. Ferris
Keyword(s):  
Positive Impact ◽  
Driving Performance ◽  
Driving Safety ◽  
Google Glass ◽  
Secondary Tasks ◽  
Voice Input ◽  
Text Input ◽  
Texting While Driving ◽  
Driving Task ◽  
The Individual

Objective: This study evaluated the individual and combined effects of voice (vs. manual) input and head-up (vs. head-down) display in a driving and device interaction task. Background: Advances in wearable technology offer new possibilities for in-vehicle interaction but also present new challenges for managing driver attention and regulating device usage in vehicles. This research investigated how driving performance is affected by interface characteristics of devices used for concurrent secondary tasks. A positive impact on driving performance was expected when devices included voice-to-text functionality (reducing demand for visual and manual resources) and a head-up display (HUD) (supporting greater visibility of the driving environment). Method: Driver behavior and performance was compared in a texting-while-driving task set during a driving simulation. The texting task was completed with and without voice-to-text using a smartphone and with voice-to-text using Google Glass’s HUD. Results: Driving task performance degraded with the addition of the secondary texting task. However, voice-to-text input supported relatively better performance in both driving and texting tasks compared to using manual entry. HUD functionality further improved driving performance compared to conditions using a smartphone and often was not significantly worse than performance without the texting task. Conclusion: This study suggests that despite the performance costs of texting-while-driving, voice input methods improve performance over manual entry, and head-up displays may further extend those performance benefits. Application: This study can inform designers and potential users of wearable technologies as well as policymakers tasked with regulating the use of these technologies while driving.

scholarly journals 2F2-4　The effect of Head Up Display on driving performance and mental workload

2016 ◽  
Vol 52 (Supplement) ◽  
pp. S404-S405
Author(s):  
Yushi FUJIWARA ◽  
Kazumitsu SHINOHARA ◽  
Takahiko KIMURA ◽  
Yasunori KINOSADA
Keyword(s):  
Mental Workload ◽  
Driving Performance

scholarly journals Driver Cognitive Distraction Detection Using Driving Performance Measures

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Lisheng Jin ◽  
Qingning Niu ◽  
Haijing Hou ◽  
Huacai Xian ◽  
Yali Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Measures ◽  
Traffic Accidents ◽  
Can Bus ◽  
Driving Performance ◽  
Cognitive State ◽  
Area Network ◽  
Characteristic Parameters ◽  
Secondary Tasks ◽  
Cognitive Distraction

Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state.

scholarly journals An Investigation of the Effects of Driver Age When Using Novel Navigation Systems in a Head-Up Display

2019 ◽  
Vol 27 (1) ◽  
pp. 32-45
Author(s):  
Sanna M. Pampel ◽  
Katherine Lamb ◽  
Gary Burnett ◽  
Lee Skrypchuk ◽  
Chrisminder Hare ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Mental Workload ◽  
Driving Performance ◽  
Older Drivers ◽  
The Other ◽  
Crash Risk ◽  
Navigation Systems ◽  
Success Rates ◽  
Age Related

Although drivers gain experience with age, many older drivers are faced with age-related deteriorations that can lead to a higher crash risk. Head-Up Displays (HUDs) have been linked to significant improvements in driving performance for older drivers by tackling issues related to aging. For this study, two Augmented Reality (AR) HUD virtual car navigation solutions were tested (one screen-fixed, one world-fixed), aiming to improve navigation performance and reduce the discrepancy between younger and older drivers by aiding the appropriate allocation of attention and easing interpretation of navigational information. Twenty-five participants (12 younger, 13 older) undertook a series of drives within a medium-fidelity simulator with three different navigational conditions (virtual car HUD, static HUD arrow graphic, and traditional head-down satnav). Results showed that older drivers tended to achieve navigational success rates similar to the younger group, but experienced higher objective mental workload. Solely for the static HUD arrow graphic, differences in most workload questionnaire items and objective workload between younger and older participants were not significant. The virtual car led to improved navigation performance of all drivers, compared to the other systems. Hence, both AR HUD systems show potential for older drivers, which needs to be further investigated in a real-world driving context.

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