Continuous Subjective Rating of Perceived Motion Incongruence During Driving Simulation

Cleij, Diane; Venrooij, Joost; Pretto, Paolo; Pool, Daan M.; Mulder, Max; Bülthoff, Heinrich H.

doi:10.1109/thms.2017.2717884

Cited by 22 publications

(12 citation statements)

References 32 publications

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“…Therefore, it will be interesting to investigate how evaluation of driving styles changes with longer term exposures. Finally, while the binary response method provided acceptable results, knowledge on the best method to use for such evaluations is currently limited and future studies should consider the value of a continuous measurement techniques, such as Likert-type scales, sliders (Schwanitz, Wittkowski, Rolny, Samel, & Basner, 2013), or knobs (Cleij et al, 2017).…”

Section: Final Conclusion and Study Limitationsmentioning

confidence: 99%

Effect of Environmental Factors and Individual Differences on Subjective Evaluation of Human-like and Conventional Automated Vehicle Controllers

Hajiseyedjavadi¹,

Boer²,

Romano³

et al. 2021

Preprint

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Achieving optimal performance in human-machine systems, such as highly automated vehicles, relies, in part, on individuals’ acceptance and use of the system, which is in turn affected by their enjoyment of engaging with, or experiencing, the system. This driving simulator study investigated individuals’ real-time subjective evaluation of four different Automated Vehicle (AV) driving styles, in different environmental contexts. Twenty-four participants were recruited to manually drive a contextually rich simulator environment, and to experience human-like and non-human-like AV driving styles, as well as the automated replay of their own manual drive. Their subjective real-time feedback towards these driving styles was analyzed. Our results showed that participants gave higher positive feedback towards the replay of their own drive, compared to the other three controllers. This difference was statistically significant, when compared to the high-speed controller (named as Fast), particularly for sharp curves. With respect to the replay of their own drive, participants gave higher negative feedback when navigating an Urban environment, compared to Rural settings. Moreover, changes in roadside furniture affected individuals’ feedback, and this effect was more prominent when the vehicle was driving closer to the edge of the road. Based on our results, we conclude that individuals’ perception of different AV driving styles changes based on different environmental conditions, including, but not limited to, road geometry and roadside furniture. These findings suggest that humans prefer a slower human-like driving style for AV controllers that adapts its speed and lateral offset to roadside objects and furniture. Investigating individual differences in AV driving style preference showed that low Sensation Seeking individuals preferred the slower human-like controller more than the faster human-like controller. Consideration of this human-centered feedback is important for the design of future AV controllers, to enhance individuals’ ride experience, and potentially improve acceptance and use of these vehicles.

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Section: Final Conclusion and Study Limitationsmentioning

confidence: 99%

Effect of Environmental Factors and Individual Differences on Subjective Evaluation of Human-like and Conventional Automated Vehicle Controllers

Hajiseyedjavadi¹,

Boer²,

Romano³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, riders in self-driving vehicles might be seated in different orientations and locations, and so conventional pedals might not provide a meaningful way of eliciting driver response. Consequently, alternatives such as a rotary knob or slider might be more appropriate mechanisms to indicate when the vehicle is going too fast or too slow (Cleij et al, 2018). More importantly, with this change in position might come diminished agency as drivers become riders.…”

Section: Discussionmentioning

confidence: 99%

“…Although pedal input is relatively natural, it lacks feedback that people might expect—the input did not affect the speed of the vehicle. Potential ways to address this limitation include having the pedal input affect the automation algorithms or using a touch screen slider or joystick as input and then displaying the magnitude of this input in a salient manner (Cleij et al, 2018; Hoffmeyer et al, 2017). The seating position and location of the rider and the configuration of the vehicle make these alternatives worthy of consideration.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Nevertheless, it is useful to conduct a human-in-the-loop experiment to identify which indicator weighs most for the overall perceived motion quality and what is the difference between indicator values which is large enough for humans to detect the quality difference. A suitable method to adopt here could be the continuous rating method, 40 where the subjects of a passive driving experiment continuously provide a feedback of the mismatch between visual and motion.…”

Section: Discussionmentioning

confidence: 99%

Objective evaluation of prediction strategies for optimization-based motion cueing

et al. 2018

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Optimization-based motion cueing algorithms based on model predictive control have been recently implemented to reproduce the motion of a car within the limited workspace of a driving simulator. These algorithms require a reference of the future vehicle motion to compute a prediction of the system response. Assumptions regarding the future reference signals must be made in order to develop effective prediction strategies. However, it remains unclear how the prediction of future vehicle dynamics influences the quality of the motion cueing. In this study two prediction strategies are considered. Oracle: the ideal prediction strategy that knows exactly what the future reference is going to be. Constant: a prediction strategy that ignores every future change and keeps the current vehicle’s linear accelerations and angular velocities constant. The two prediction strategies are used to reproduce a sequence of maneuvers between 0 and 50 km/h. A comparative analysis is carried out to objectively evaluate the influence of the prediction strategies on motion cueing quality. Dedicated indicators of correlation, delay and absolute error are used to compare the effects of the adopted prediction on simulator motion. Also the motion cueing mechanisms adopted by the different conditions are analyzed, together with the usage of simulator workspace. While the constant strategy provided reasonable cueing quality, the results show that knowledge of the future vehicle trajectory reduces the delay and improves correlation with the reference trajectory, it allows the combined usage of different motion cueing mechanisms and increases the usage of workspace.

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Continuous Subjective Rating of Perceived Motion Incongruence During Driving Simulation

Abstract: This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

Cited by 22 publications

References 32 publications

Effect of Environmental Factors and Individual Differences on Subjective Evaluation of Human-like and Conventional Automated Vehicle Controllers

Effect of Environmental Factors and Individual Differences on Subjective Evaluation of Human-like and Conventional Automated Vehicle Controllers

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

Objective evaluation of prediction strategies for optimization-based motion cueing

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