Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time

2018 ◽  
Vol 60 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Eric T. Greenlee ◽  
Patricia R. DeLucia ◽  
David C. Newton
Keyword(s):  
Safety Concern ◽  
Reaction Times ◽  
Vigilance Task ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Hazard Detection ◽  
And Performance

Objective: The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Background: Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Method: Participants “drove” a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. Results: As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Conclusion: Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. Application: To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

Driver Vigilance in Automated Vehicles: Effects of Demands on Hazard Detection Performance

2018 ◽  
Vol 61 (3) ◽  
pp. 474-487 ◽  
Author(s):  
Eric T. Greenlee ◽  
Patricia R. DeLucia ◽  
David C. Newton
Keyword(s):  
Detection Performance ◽  
Task Demands ◽  
Spatial Uncertainty ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Automated Vehicles ◽  
Hazard Detection ◽  
High Workload ◽  
And Performance ◽  
The Impact

Objective: The current study investigated driver vigilance in partially automated vehicles to determine whether increased task demands reduce a driver’s ability to monitor for automation failures and whether the vigilance decrement associated with hazard detections is due to driver overload. Background: Drivers of partially automated vehicles are expected to monitor for signs of automation failure. Previous research has shown that a driver’s ability to perform this duty declines over time. One possible explanation for this vigilance decrement is that the extreme demands of vigilance causes overload and leads to depletion of limited attentional resources required for vigilance. Method: Participants completed a 40-min drive in a simulated partially automated vehicle and were tasked with monitoring for hazards that represented potential automation failures. Two factors were manipulated to test the impact of monitoring demands on performance: Spatial uncertainty and event rate. Results: As predicted, hazard detection performance was poorer when monitoring demands were increased, and performance declined as a function of time on task. Subjective reports also indicated high workload and task-induced stress. Conclusion: Drivers of partially automated vehicles are impaired by the vigilance decrement and elevated task demands, meaning that safe operation becomes less likely when the demands associated with monitoring automation increase and as a drive extends in duration. This study also supports the notion that vigilance performance in partially automated vehicles is likely due to driver overload. Application: Developers of automation technologies should consider countermeasures that attenuate a driver’s cognitive load when tasked with monitoring automation.

scholarly journals The Sustained Attention to Response Task Shows Lower Cingulo-Opercular and Frontoparietal Activity in People with Narcolepsy Type 1: An fMRI Study on the Neural Regulation of Attention

2020 ◽  
Vol 10 (7) ◽  
pp. 419
Author(s):  
Jari K. Gool ◽  
Ysbrand D. van der Werf ◽  
Gert Jan Lammers ◽  
Rolf Fronczek
Keyword(s):  
Sustained Attention ◽  
Group Performance ◽  
Brain Activity ◽  
Reaction Times ◽  
Vigilance Task ◽  
Error Rates ◽  
Time On Task ◽  
Related Activity ◽  
Response Task

Vigilance complaints often occur in people with narcolepsy type 1 and severely impair effective daytime functioning. We tested the feasibility of a three-level sustained attention to response task (SART) paradigm within a magnetic resonance imaging (MRI) environment to understand brain architecture underlying vigilance regulation in individuals with narcolepsy type 1. Twelve medication-free people with narcolepsy type 1 and 11 matched controls were included. The SART included four repetitions of a baseline block and two difficulty levels requiring moderate and high vigilance. Outcome measures were between and within-group performance indices on error rates and reaction times, and functional MRI (fMRI) parameters: mean activity during the task and between-group activity differences across the three conditions and related to changes in activation over time (time-on-task) and error-related activity. Patients—but not controls—made significantly more mistakes with increasing difficulty. The modified SART is a feasible MRI vigilance task showing similar task-positive brain activity in both groups within the cingulo-opercular, frontoparietal, arousal, motor, and visual networks. During blocks of higher vigilance demand, patients had significantly lower activation in these regions than controls. Patients had lower error-related activity in the left pre- and postcentral gyrus. The time-on-task activity differences between groups suggest that those with narcolepsy are insufficiently capable of activating attention- and arousal-related regions when transitioning from attention initiation to stable attention, specifically when vigilance demand is high. They also show lower inhibitory motor activity in relation to errors, suggesting impaired executive functioning.

Performance in Vigilance Tasks is Related to Both State and Contextual Motivation

2016 ◽  
Vol 60 (1) ◽  
pp. 1145-1149 ◽  
Author(s):  
Alexis R. Dewar ◽  
Nicholas W. Fraulini ◽  
Victoria L. Claypoole ◽  
James L. Szalma
Keyword(s):  
Intrinsic Motivation ◽  
Sustained Attention ◽  
Vigilance Task ◽  
False Alarms ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Vigilance Performance ◽  
Task Instructions ◽  
State Motivation

Vigilance, or sustained attention, is the ability to maintain attention to stimuli over a prolonged period of time. Synonymous with the study of sustained attention is the vigilance decrement, which is a decline in performance as a function of time on task. In the present study, we examined the effects of state motivation (i.e., motivation measured immediately prior to the task) and context-based motivation (i.e., motivation that stems from task instructions) on vigilance performance in a sensory-based vigilance task. Forty-three participants completed a 24-minute vigilance task, as well as measures of stress and workload. The results indicated that those higher in state intrinsic motivation and motivating instructions outperformed their peers in terms of hits and false alarms. We conclude that motivation may help facilitate vigilant attention.

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

scholarly journals Modeling Cross-National Differences in Automated Vehicle Acceptance

2020 ◽  
Vol 12 (22) ◽  
pp. 9765
Author(s):  
Shelly Etzioni ◽  
Jamil Hamadneh ◽  
Arnór B. Elvarsson ◽  
Domokos Esztergár-Kiss ◽  
Milena Djukanovic ◽  
...  
Keyword(s):  
Mode Choice ◽  
Continuous Distribution ◽  
Preference Heterogeneity ◽  
Type Model ◽  
Model Parameters ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
National Differences ◽  
Private Car

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed.

scholarly journals Propensity to intentional and unintentional mind-wandering differs in arousal and executive vigilance tasks

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258734
Author(s):  
Víctor Martínez-Pérez ◽  
Damián Baños ◽  
Almudena Andreu ◽  
Miriam Tortajada ◽  
Lucía B. Palmero ◽  
...  
Keyword(s):  
Vigilance Task ◽  
Mind Wandering ◽  
Progressive Increase ◽  
Resource Control ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Task Execution ◽  
Attentional Resources ◽  
Response Task ◽  
Psychomotor Vigilance

We typically observe a decrement in vigilance with time-on-task, which favors the propensity for mind-wandering, i.e., the shifting of attention from the task at hand to task-unrelated thoughts. Here, we examined participants’ mind-wandering, either intentional or unintentional, while performing vigilance tasks that tap different components of vigilance. Intentional mind-wandering is expected mainly when the arousal component is involved, whereas unintentional mind-wandering is expected mainly in tasks involving the executive component. The Psychomotor Vigilance Task (PVT) assessed the arousal component, whereas the Sustained Attention to Response task (SART) assessed the executive component of vigilance. The two types of mind-wandering were probed throughout task execution. The results showed that the overall rate of mind-wandering was higher in the PVT than in the SART. Intentional mind-wandering was higher with the PVT than with the SART, whereas unintentional mind-wandering was higher with the SART than with the PVT. Regarding mind-wandering as a function of vigilance decrement with time-on-task, unintentional mind-wandering in the PVT increased between blocks 1 and 2 and then stabilized, whereas a progressive increase was observed in the SART. Regarding intentional mind-wandering, a progressive increase was only observed in the SART. The differential patterns of intentional and unintentional mind-wandering in both tasks suggest that, intentional mind wandering occurs mainly in arousal tasks in which propensity to mind-wander has little impact on task performance. However, unintentional mind-wandering occurs mainly in executive tasks as a result of a failure of cognitive control, which promotes attentional resources to be diverted toward mind-wandering. These results are discussed in the context of the resource-control model of mind-wandering.

Assessing Drivers’ Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude

2019 ◽  
pp. 001872081988036 ◽  
Author(s):  
John D. Lee ◽  
Shu-Yuan Liu ◽  
Joshua Domeyer ◽  
Azadeh DinparastDjadid
Keyword(s):  
Mixed Model ◽  
Driving Simulator ◽  
Statistical Technique ◽  
Brake Pedal ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Accelerator Pedal ◽  
Driving Style ◽  
Automated Vehicle ◽  
Stop Sign

Objective: This study examines how driving styles of fully automated vehicles affect drivers’ trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers’ interventions. Background: Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle’s driving style might have an important influence. Method: A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation’s driving style and the person’s driving style affected the frequency and magnitude of their pedal depression. Results: The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion: Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers’ trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application: We offer a measure and method for assessing driving styles.

scholarly journals Designing Automated Vehicle Interiors for Different Cultures: Evidence From China, Germany, and the United States

2020 ◽  
pp. 106480462096615
Author(s):  
Vanessa Sauer ◽  
Alexander Mertens ◽  
Stefan Groß ◽  
Jens Heitland ◽  
Verena Nitsch
Keyword(s):  
United States ◽  
Well Being ◽  
The United States ◽  
Vehicle Design ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Global Trend ◽  
Design Requirements ◽  
Different Cultures

The advent of automated driving is a global trend. It is likely that views on what will make an automated vehicle trustworthy, comfortable, usable, and enhance passengers’ well-being while driving will differ between markets. Therefore, we conducted an expert survey ( n = 28) to identify cultural-specific design requirements of Level 4 automated vehicles for China, Germany, and the United States. Our results indicate a tendency toward hedonic vehicle design in China and pragmatic design in Germany. United States lies between these two markets. The results imply that car manufacturers can influence passengers’ well-being through vehicle design and, in turn, increase acceptance of automated vehicles.

Evaluating feedback requirements for trust calibration in automated vehicles

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Philipp Wintersberger ◽  
Frederica Janotta ◽  
Jakob Peintner ◽  
Andreas Löcken ◽  
Andreas Riener
Keyword(s):  
Scientific Basis ◽  
Market Penetration ◽  
Feedback Systems ◽  
Automated Vehicles ◽  
Structured Interviews ◽  
Automated Driving ◽  
Major Barrier ◽  
Automated Vehicle ◽  
Inappropriate Use ◽  
Open Question

Abstract The inappropriate use of automation as a result of trust issues is a major barrier for a broad market penetration of automated vehicles. Studies so far have shown that providing information about the vehicle’s actions and intentions can be used to calibrate trust and promote user acceptance. However, how such feedback could be designed optimally is still an open question. This article presents the results of two user studies. In the first study, we investigated subjective trust and user experience of (N=21) participants driving in a fully automated vehicle, which interacts with other traffic participants in virtual reality. The analysis of questionnaires and semi-structured interviews shows that participants request feedback about the vehicle’s status and intentions and prefer visual feedback over other modalities. Consequently, we conducted a second study to derive concrete requirements for future feedback systems. We showed (N=56) participants various videos of an automated vehicle from the ego perspective and asked them to select elements in the environment they want feedback about so that they would feel safe, trust the vehicle, and understand its actions. The results confirm a correlation between subjective user trust and feedback needs and highlight essential requirements for automatic feedback generation. The results of both experiments provide a scientific basis for designing more adaptive and personalized in-vehicle interfaces for automated driving.

scholarly journals Validation of a driving simulator for research into human factors issues of automated vehicles

2019 ◽  
Vol 30 (2) ◽  
pp. 37-44
Author(s):  
Nebojsa Tomasevic ◽  
Tim Horberry ◽  
Brian Fildes
Keyword(s):  
Statistical Analysis ◽  
Human Factors ◽  
Driving Simulator ◽  
Research Centre ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Simulated Driving ◽  
Automated Vehicle ◽  
Perception Of Safety ◽  
Accident Research

This study evaluated the behavioural validity of the Monash University Accident Research Centre automation driving simulator for research into the human factors issues associated with automated driving. The study involved both on-road and simulated driving. Twenty participants gave ratings of their willingness to resume control of an automated vehicle and perception of safety for a variety of situations along the drives. Each situation was individually categorised and ratings were processed. Statistical analysis of the ratings confirmed the behavioural validity of the simulator, in terms of the similarity of the on-road and simulator data.

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