Psychophysics of Trust in Vehicle Control Algorithms

Price, Morgan; Venkatraman, Vindhya; Gibson, Madeleine; Lee, John; Mutlu, Bilge

doi:10.4271/2016-01-0144

Cited by 21 publications

(23 citation statements)

References 12 publications

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“…Instructions could prove beneficial, however further steps should be taken to continuously demonstrate the capability of the automation to drivers (8) . Consistent with related research in human-robot interaction (9) , vehicle control algorithms are a promising solution to conveying vehicle capabilities and limits to drivers (10) . Vehicle control algorithms are the control laws that govern vehicle motion.…”

Section: Introductionmentioning

confidence: 64%

“…The lane keeping and adaptive algorithms, combined with instructions, were effective in directing attention to the road. However, the lane keeping algorithm was perceived as untrustworthy in a previous study (10) , and the haptic and vestibular cues that would accompany these algorithms in actual cars could accentuate this discomfort. In addition, extended exposure to this behavior might lead drivers to reject these algorithms, a possibility that the short exposure to the algorithm in this study failed to address.…”

Section: Vehicle Design Implicationsmentioning

confidence: 84%

“…The vehicle control algorithms were developed using Matlab Simulink™ and interfaced with the NADS Dyna, the vehicle dynamics module of the simulator. Using findings from a previous study (10) , the steering behavior of the automation was controlled using a proportional-integral-derivative (PID) controller. The steering angle was calculated using the instantaneous lateral error of the vehicle's center of gravity from the lane center.…”

Section: Vehicle Control Algorithmsmentioning

confidence: 99%

“…Lane centering algorithms track the center of the lane, whereas lane keeping algorithms allow the vehicle to drift within the lane boundaries. Even in a fixed-base simulator, adjusting algorithms to vary between lane keeping and lane centering strongly influenced driver trust in the automation (10) . Algorithms that track the center of the lane garnered more trust, which would be appropriate for highly capable automation (e.g., Level 4).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effect of Automation Instructions and Vehicle Control Algorithms on Eye Behavior in Highly Automated Vehicles

Price

Lee

Dinparastdjadid

et al. 2019

IJAE

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Increasingly vehicle automation may convey greater capability than it actually possesses. The emergence of highly capable vehicle automation (e.g., SAE Level 4) and the promise of driverless vehicles in the near future can lead drivers to inappropriately cede responsibility for driving to the vehicle with less capable automation (e.g., SAE Level 2). This inappropriate reliance on automation can compromise safety, and so we investigated how algorithms and instructions might mitigate overreliance. Seventy-two drivers, balanced by gender, between the ages of 25 and 55, participated in this study using a fixed-base driving simulator. Drivers were exposed to one of three vehicle steering algorithms: lane centering, lane keeping, or an adaptive combination. A gaze tracker was used to track eye glance behavior. While automation was engaged, participants were told they could interact with an email sorting task on a tablet positioned near the center stack. Changes in roadway demand-traffic approaching in the adjacent lane-varied across the drive. Instructions indicating the driver was responsible, in combination with the adaptive algorithm, led drivers to be particularly attentive to the road as the traffic approached them. These results also have implications for evaluating more capable automation (SAE Levels 4 and 5), where drivers need not attend to the road: unnecessary attention to roadway demands might indicate lack of trust and acceptance of control algorithms that guide driverless vehicles.

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Section: Introductionmentioning

confidence: 64%

Section: Vehicle Design Implicationsmentioning

confidence: 84%

Section: Vehicle Control Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Automation Instructions and Vehicle Control Algorithms on Eye Behavior in Highly Automated Vehicles

Price

Lee

Dinparastdjadid

et al. 2019

IJAE

Self Cite

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show abstract

“…Likewise, Seppelt and Lee (2007) promote and found benefits of an ecological interface design that makes limits and behaviour of ACC visible via emergent displays of continuous information (time headway, time to collision and range rate) that relates the present vehicle to other vehicles across different dynamically evolving traffic contexts. In terms of an SAE Level 2 simulation, participants in Price et al (2016) observed automated lateral and longitudinal control where vehicle capability was indicated via physically embodied lateral control algorithms (tighter/looser lane centre adherence) as opposed to via typical visual and auditory warnings. Consequently, drivers' trust was found to be sensitive to such a situated communication of automation capability.…”

Section: Solution Area (6): Support the Role From The Triadic Ecologimentioning

confidence: 99%

How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas

Cabrall

Eriksson

Dreger

et al. 2019

Theoretical Issues in Ergonomics Science

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This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/ training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation. Relevance to human factors/Relevance to ergonomics theory A good amount of human factors research has already previously been devoted to examining human vigilance in supervising automated process (both by experimental investigation and literature review synthesis). However, given recent advances and accidents with humans ARTICLE HISTORY

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Do Drivers Vary in Preferences for Automated Driving Styles Across Different Scenarios? Evidence from a Simulation Experiment

Zhang,

Wang,

et al. 2023

Lecture Notes in Computer Science

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Psychophysics of Trust in Vehicle Control Algorithms

Cited by 21 publications

References 12 publications

Effect of Automation Instructions and Vehicle Control Algorithms on Eye Behavior in Highly Automated Vehicles

Effect of Automation Instructions and Vehicle Control Algorithms on Eye Behavior in Highly Automated Vehicles

How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas

Do Drivers Vary in Preferences for Automated Driving Styles Across Different Scenarios? Evidence from a Simulation Experiment

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