Displays for Productive Non-Driving Related Tasks: Visual Behavior and Its Impact in Conditionally Automated Driving

(1) Background: Primary driving tasks are increasingly being handled by vehicle automation so that support for non-driving related tasks (NDRTs) is becoming more and more important. In SAE L3 automation, vehicles can require the driver-passenger to take over driving controls, though. Interfaces for NDRTs must therefore guarantee safe operation and should also support productive work. (2) Method: We conducted a within-subjects driving simulator study (N=53) comparing Heads-Up Displays (HUDs) and Auditory Speech Displays (ASDs) for productive NDRT engagement. In this article, we assess the NDRT displays’ effectiveness by evaluating eye-tracking measures and setting them into relation to workload measures, self-ratings, and NDRT/take-over performance. (3) Results: Our data highlights substantially higher gaze dispersion but more extensive glances on the road center in the auditory condition than the HUD condition during automated driving. We further observed potentially safety-critical glance deviations from the road during take-overs after a HUD was used. These differences are reflected in self-ratings, workload indicators and take-over reaction times, but not in driving performance. (4) Conclusion: NDRT interfaces can influence visual attention even beyond their usage during automated driving. In particular, the HUD has resulted in safety-critical glances during manual driving after take-overs. We found this impacted workload and productivity but not driving performance.

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Comparing treatment effects of oral THC on simulated and on-the-road driving performance: testing the validity of driving simulator drug research

Psychopharmacology ◽

10.1007/s00213-015-3927-9 ◽

2015 ◽

Vol 232 (16) ◽

pp. 2911-2919 ◽

Cited By ~ 16

Author(s):

J. L. Veldstra ◽

W. M. Bosker ◽

D. de Waard ◽

J. G. Ramaekers ◽

K. A. Brookhuis

Keyword(s):

Treatment Effects ◽

Driving Simulator ◽

Drug Research ◽

Performance Testing ◽

Driving Performance ◽

The Road ◽

On The Road

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0136 Bright Light Could Be an Alternative to the Caffeine for Improving Driving Performance in Chronically Sleep-Deprived Young Drivers

SLEEP ◽

10.1093/sleep/zsaa056.134 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A53-A54

Author(s):

S Shekari Soleimanloo ◽

V Garcia-Hansen ◽

M White ◽

S S Smith

Keyword(s):

Bright Light ◽

Driving Performance ◽

Baseline Condition ◽

The Road ◽

Circadian Phase ◽

Young Drivers ◽

Subjective Sleepiness ◽

Within Subjects ◽

Increased Sensitivity ◽

On The Road

Abstract Introduction Young drivers are over-involved in sleepiness-related crashes. Daytime and nighttime exposure to light might shift the human circadian phase and alertness. The alerting effects of bright light were compared with those of caffeine in young drivers. Methods In a within-subjects study, 30 chronically sleep-deprived non-professional drivers (aged 18–24 years) completed two simulated daytime driving sessions per day across three consecutive days. Participants completed the first drive under a Baseline condition (non-caffeinated gum, 555 nm light, 0.3 µW/cm²), and the second drive under the randomized conditions of Light (500 nm, 230 µW/cm²), Caffeine (100 mg caffeinated gum) or the combination of Light and Caffeine. Using mixed-effects models, the alerting effects of these conditions on objective sleepiness (ECG beat-to-beat intervals), driving performance (lateral lane variability) and subjective sleepiness (scores on the Karolinska sleepiness scale; KSS) were examined. Results Compared to the Baseline condition, lateral lane variability decreased under the Light (P=0.011), Caffeine (P=0.0001), and the combination of Light and Caffeine (P=0.046). Lateral lane variability was lower under Caffeine when compared with the Light (P=0.009) or the combination of Light and Caffeine (P= 0.0001). Average beat-to-beat intervals increased from the Baseline condition to the Light (P=0.017), Caffeine (P=0. 0.0001), and the combination of Light and Caffeine conditions (P=0.0001). All three conditions significantly reduced subjective sleepiness compared to the Baseline condition (KSS= 4–5 vs KSS=6, P= 0.0001). Conclusion Bright light, either alone or combined with caffeine, improves driving performance and subjective sleepiness during daytime drives. Light might better improve objective sleepiness and other sleepiness indicators during nighttime when drivers are sleepier and have an increased sensitivity to the light. Further research would clarify how the circadian effects are aligned with the alerting effects of the light. Bright light, as an alternative to or combined with caffeine, could reduce sleep-related crashes on the road. Support NA

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Behavioral Changes to Repeated Takeovers in Highly Automated Driving: Effects of the Takeover-Request Design and the Nondriving-Related Task Modality

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720818814963 ◽

2018 ◽

Vol 61 (5) ◽

pp. 839-849 ◽

Cited By ~ 6

Author(s):

Fabienne Roche ◽

Anna Somieski ◽

Stefan Brandenburg

Keyword(s):

Subjective Experience ◽

Vehicle Control ◽

Behavioral Changes ◽

Behavior Changes ◽

Automated Driving ◽

The Road ◽

Gaze Patterns ◽

On The Road ◽

Task Modality ◽

Highly Automated Driving

Objective: We investigated drivers’ behavior and subjective experience when repeatedly taking over their vehicles’ control depending on the design of the takeover request (TOR) and the modality of the nondriving-related task (NDRT). Background: Previous research has shown that taking over vehicle control after highly automated driving provides several problems for drivers. There is evidence that the TOR design and the NDRT modality may influence takeover behavior and that driver behavior changes with more experience. Method: Forty participants were requested to resume control of their simulated vehicle six times. The TOR design (auditory or visual-auditory) and the NDRT modality (auditory or visual) were varied. Drivers’ takeover behavior, gaze patterns, and subjective workload were recorded and analyzed. Results: Results suggest that drivers change their behavior to the repeated experience of takeover situations. An auditory TOR leads to safer takeover behavior than a visual-auditory TOR. And with an auditory TOR, the takeover behavior improves with experience. Engaging in the visually demanding NDRT leads to fewer gazes on the road than the auditory NDRT. Participants’ fixation duration on the road decreased over the three takeovers with the visually demanding NDRT. Conclusions: The results imply that (a) drivers change their behavior to repeated takeovers, (b) auditory TOR designs might be preferable over visual-auditory TOR designs, and (c) auditory demanding NDRTs allow drivers to focus more on the driving scene. Application: The results of the present study can be used to design TORs and determine allowed NDRTs in highly automated driving.

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Advance Information on the Road: A Simulator Study of the Effect of Road Markings

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/001872087601800601 ◽

1976 ◽

Vol 18 (6) ◽

pp. 521-532 ◽

Cited By ~ 2

Author(s):

Harald Witt ◽

Carl G. Hoyos

Keyword(s):

Driving Simulator ◽

Driving Behavior ◽

Speed Profile ◽

The Road ◽

Positive Effects ◽

Road Signs ◽

Advance Information ◽

Simulator Study ◽

On The Road ◽

Accident Statistics

Accident statistics and studies of driving behavior have shown repeatedly that curved roads are hazardous. It was hypothesized that the safety of curves could be improved by indicating in advance the course of the road in a more effective way than do traditional road signs. A code of sequences of stripes put on right edge of the pavement was developed to indicate to the driver the radius of the curve ahead. The main characteristic of this code was the frequency of transitions from code elements to gaps between elements. The effect of these markings was investigated on a driving simulator. Twelve subjects drove on simulated roads of different curvature and with different placement of the code in the approach zone. Some positive effects of the advance information could be observed. The subjects drove more steadily, more precisely, and with a more suitable speed profile.

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Good Intentions? Willingness to Engage with Technology on the Road and in a Driving Simulator

Driver Distraction ◽

10.1201/9780429466809-8 ◽

2018 ◽

pp. 147-176 ◽

Cited By ~ 1

Author(s):

Katie J. Parnell ◽

Neville A. Stanton ◽

Katherine L. Plant

Keyword(s):

Driving Simulator ◽

The Road ◽

On The Road ◽

Willingness To Engage

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Drivers' Brake Reaction Times

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/001872087101300104 ◽

1971 ◽

Vol 13 (1) ◽

pp. 23-27 ◽

Cited By ~ 188

Author(s):

Gunnar Johansson ◽

Kåre Rumar

Keyword(s):

Reaction Time ◽

Small Group ◽

Correction Factor ◽

Reaction Times ◽

Large Group ◽

The Road ◽

On The Road

The object of this investigation was to determine the distribution of brake reaction times which can be expected from drivers who have to brake suddenly and completely unexpectedly in traffic situations. The experiments were carried out as follows: 1. Brake reaction time was measured on a large group of drivers (321), in an anticipated situation on the road (Brake reaction time 1). 2. A small group of drivers (5) was repeatedly tested in the same way (Brake reaction time 2). 3. The same small group was repeatedly tested in a surprise situation (Brake reaction time 3). 4. The ratio of brake reaction time 3 to brake reaction time 2 was used as a correction factor and applied to brake reaction time 1. The corrected median of the resulting distribution was 0.9 sec.; 25% of the group was estimated to have a brake reaction time longer than 1.2 sec.

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

10.31234/osf.io/bwp6f ◽

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.

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Performance on the Road: Examining the Critical Role of Training Effectiveness Amongst the Blue-Collar Employees in Bahrain

Asian Journal of Empirical Research ◽

10.18488/journal.1007/2018.8.9/1007.9.342.351 ◽

2018 ◽

Vol 8 (9) ◽

pp. 342-351

Author(s):

Umair Ahmed ◽

Soleman Mozammel ◽

Habib Ahmed

Keyword(s):

Structural Equation ◽

Training Programs ◽

Employee Performance ◽

Critical Role ◽

Driving Performance ◽

Training Effectiveness ◽

Blue Collar ◽

Equation Modeling ◽

The Road ◽

On The Road

The present study attempted to investigate the role or training effectiveness on the employee performance of blue-collar employees working in Bahrain Transportation sector. Therein, the study examined the effectiveness of five different training programs offered to large vehicle drivers serving in the transportation industry. A total of 135 bus drivers from Alsadiq transport company Bahrain were sampled for the study to outline the effectiveness of three major training programs that they were offered including Customer Handling & Passenger Safety, Health & Hygiene and Guide to traffic laws and driving in Bahrain on their driving performance. Structural equation modeling using smart PLS was used for the present study for data analysis and interpretation. The study found a significant relationship impact of two programs on their driving performance of the respondents. The study forwards theoretical and practical implications and scope for further studies.

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