scholarly journals Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Haiwei Wang ◽  
Jianrong Liu ◽  
Feng You
Keyword(s):  
Driving Simulator ◽  
Mental Workload ◽  
Rapid Development ◽  
Nonparametric Test ◽  
Nonparametric Tests ◽  
Risk Identification ◽  
Physiological Characteristics ◽  
Physiological Data ◽  
Mountain Area ◽  
Driving Task

With the rapid development of advanced mobile intelligent terminals, driving tasks are diverse, and new traffic safety problems occur. We propose a new research on physiological characteristics and nonparametric tests for the master-slave driving task, especially for evaluation of drivers’ mental workload in mountain area highway in nighttime scenario. First, we establish the experimental platform based driving simulator and design the master-slave driving task. Second, based on the physiological data and subjective evaluation for mental workload, we use statistical methods to composite the physical changes evolution analysis in a driving simulator. Finally, we finished nonparametric test of the drivers’ psychological load and road test. The results show that in compassion with the daytime scenario, drivers should pay much effort to driving skills and risk identification in the nighttime scenario. Thus, in the same driving condition, drivers should bear the higher level of mental workload, and it has been subjected to even greater pressures and intensity of emotions.

Comparison of Five Mental Workload Assessment Procedures in a Moving-Base Driving Simulator

1979 ◽  
Vol 21 (2) ◽  
pp. 129-143 ◽  
Author(s):  
Thomas G. Hicks ◽  
Walter W. Wierwille
Keyword(s):  
Task Performance ◽  
Cardiac Arrhythmia ◽  
Secondary Task ◽  
Rating Scales ◽  
Driving Simulator ◽  
Mental Workload ◽  
Degree Of Freedom ◽  
Lateral Deviation ◽  
Subjective Opinion ◽  
Driving Task

Five methods of measuring mental workload (secondary task performance, visual occlusion, cardiac arrhythmia, subjective opinion rating scales, and primary task performance) were compared for sensitivity to changes in operator loading. Each was used to differentiate among low, medium, and high levels of workload defined in terms of the application point of crosswind gusts in a driving task. The driving task was produced using an automobile driving simulator with a six-degree of freedom computer generated display, a four-degree of freedom physical motion system, and a four-channel sound system. Techniques of mental workload measurement that have shown promise in previous studies were used as a between-subjects factor, and subjects were presented with a within-subject factor of wind gust placement. Gusts at the front of the vehicle represented high workload levels, and gusts toward the center of the vehicle represented progressively lower levels of workload. The results showed significant differences among workload levels for subjective opinion scales and primary performance measures of lateral deviation, yaw deviation, and steering reversals. A relative sensitivity estimate of these would be, from highest to lowest sensitivity, steering reversals and yaw deviation, rating scales, and lateral deviation. The techniques of occlusion, cardiac arrhythmia, and secondary task performance yielded no significant workload effect.

scholarly journals Validity of Mental Workload Measures in a Driving Simulation Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Francesco Galante ◽  
Fabrizio Bracco ◽  
Carlo Chiorri ◽  
Luigi Pariota ◽  
Luigi Biggero ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Mental Workload ◽  
Critical Issue ◽  
Simulation Environment ◽  
Driving Simulators ◽  
The Road ◽  
Mixed Support ◽  
Vehicle Systems ◽  
Driving Task ◽  
On The Road

Automated in-vehicle systems and related human-machine interfaces can contribute to alleviating the workload of drivers. However, each new functionality can also introduce a new source of workload, due to the need to attend to new tasks and thus requires careful testing before being implemented in vehicles. Driving simulators have become a viable alternative to on-the-road tests, since they allow optimal experimental control and high safety. However, for each driving simulator to be a useful research tool, for each specific task an adequate correspondence must be established between the behavior in the simulator and the behavior on the road, namely, the simulator absolute and relative validity. In this study we investigated the validity of a driving-simulator-based experimental environment for research on mental workload measures by comparing behavioral and subjective measures of workload of the same large group of participants in a simulated and on-road driving task on the same route. Consistent with previous studies, mixed support was found for both types of validity, although results suggest that allowing more and/or longer familiarization sessions with the simulator may be needed to increase its validity. Simulator sickness also emerged as a critical issue for the generalizability of the results.

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.

scholarly journals Drivers’ Attention Strategies before Eyes-off-Road in Different Traffic Scenarios: Adaptation and Anticipation

2021 ◽  
Vol 18 (7) ◽  
pp. 3716
Author(s):  
Zhuofan Liu ◽  
Wei Yuan ◽  
Yong Ma
Keyword(s):  
Visual Attention ◽  
Driving Simulator ◽  
Test Drive ◽  
Urban Road ◽  
Strong Shift ◽  
Related Information ◽  
Road Users ◽  
Urban Rural ◽  
Within Subjects ◽  
Driving Task

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.

A Driving Simulator Study of the Performance Effects of Low Blood Alcohol Concentration

1996 ◽  
Vol 40 (18) ◽  
pp. 943-946 ◽  
Author(s):  
R. Wade Allen ◽  
Zareh Parseghian ◽  
Anthony C. Stein
Keyword(s):  
Divided Attention ◽  
Driving Simulator ◽  
Blood Alcohol Concentration ◽  
Large Body ◽  
Alcohol Concentration ◽  
Blood Alcohol ◽  
Performance Effects ◽  
Driving Task ◽  
Simulator Study ◽  
And Performance

There is a large body of research that documents the impairing effect of alcohol on driving behavior and performance. Some of the most significant alcohol influence seems to occur in divided attention situations when the driver must simultaneously attend to several aspects of the driving task. This paper describes a driving simulator study of the effect of a low alcohol dose, .055 BAC (blood alcohol concentration %/wt), on divided attention performance. The simulation was mechanized on a PC and presented visual and auditory feedback in a truck cab surround. Subjects were required to control speed and steering on a rural two lane road while attending to a peripheral secondary task. The subject population was composed of 33 heavy equipment operators who were tested during both placebo and drinking sessions. Multivariate Analysis of Variance showed a significant and practical alcohol effect on a range of variables in the divided attention driving task.

The Effects of Telematics on Driver Distraction

2002 ◽  
Vol 46 (22) ◽  
pp. 1868-1870 ◽  
Author(s):  
Patrick Siebert ◽  
Mustapha Mouloua ◽  
Kendra Burns ◽  
Jennifer Marino ◽  
Lora Scagliola ◽  
...  
Keyword(s):  
Secondary Task ◽  
Cell Phone ◽  
Driving Simulator ◽  
Systems Design ◽  
Driver Distraction ◽  
Cellular Phones ◽  
Driver Safety ◽  
A Cell ◽  
Driving Task ◽  
Simulated Driving Task

This study used both cellular phones and analogue radio to measure driver distraction and workload in a low fidelity driving simulator. Thirty-four participants performed a simulated driving task while using either a cell phone or a radio in conjunction with a secondary task assessing their spare attentional capacity. The results showed that more lane deviations were made during the cell phone and radio tuning use than both of the pre-allocation and Post-allocation phases. The secondary task errors were also higher during both the cell phone and radio tuning allocation phase than the pre-allocation and post-allocation phases. These findings indicate the greater workload load levels associated with the use of telemetric devices. These findings have major implications for driver safety and telemetric systems design.

scholarly journals Privacy-Preserving Efficient Data Retrieval in IoMT Based on Low-Cost Fog Computing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Na Wang ◽  
Yuanyuan Cai ◽  
Junsong Fu ◽  
Jie Xu
Keyword(s):  
Energy Cost ◽  
Data Privacy ◽  
Low Cost ◽  
Rapid Development ◽  
Fog Computing ◽  
Bat Algorithm ◽  
Data Retrieval ◽  
High Energy ◽  
Physiological Data ◽  
Range Tree

The rapid development of Internet of Medical Things (IoMT) is remarkable. However, IoMT faces many problems including privacy disclosure, long delay of service orders, low retrieval efficiency of medical data, and high energy cost of fog computing. For these, this paper proposes a data privacy protection and efficient retrieval scheme for IoMT based on low-cost fog computing. First, a fog computing system is located between a cloud server and medical workers, for processing data retrieval requests of medical workers and orders for controlling medical devices. Simultaneously, it preprocesses physiological data of patients uploaded by IoMT, collates them into various data sets, and transmits them to medical institutions in this way. It makes the entire execution process of low latency and efficient. Second, multidimensional physiological data are of great value, and we use ciphertext retrieval to protect privacy of patient data in this paper. In addition, this paper uses range tree to build an index for storing physiological data vectors, and meanwhile a range retrieval method is also proposed to improve data search efficiency. Finally, bat algorithm (BA) is designed to allocate cost on a fog server group for significant energy cost reduction. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

scholarly journals Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1029 ◽  
Author(s):  
Thomas Kundinger ◽  
Nikoletta Sofra ◽  
Andreas Riener
Keyword(s):  
Machine Learning ◽  
User Study ◽  
Driving Simulator ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Physiological Data ◽  
Promising Alternative ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Medical Grade

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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