Systematic Observation of an Expert Driver's Gaze Strategy—An On-Road Case Study

Lappi, Otto; Rinkkala, Paavo; Pekkanen, Jami

doi:10.3389/fpsyg.2017.00620

Cited by 51 publications

(50 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a monocular eye-tracking setup with one eye camera and two glints, although Pupil Capture does not use glint information for gaze tracking. It was chosen because the Pupil-labs platform is a popular low-cost alternative to the Tobii and SMI eye-tracking setups, and it is increasingly used in head-worn eye-tracking studies (e.g., Li, Kearney, Braithwaite, & Lin, 2018;Lappi, Rinkkala, & Pekkanen, 2017;Zhao, Salesse, Marin, Gueugnon, & Bardy, 2017). Pupil-labs explicitly claims that their software compensates for eye-tracker slippage in 3D mode, 2 which is the default recording mode.…”

Section: Apparatusmentioning

confidence: 99%

The impact of slippage on the data quality of head-worn eye trackers

et al. 2020

View full text Add to dashboard Cite

Mobile head-worn eye trackers allow researchers to record eye-movement data as participants freely move around and interact with their surroundings. However, participant behavior may cause the eye tracker to slip on the participant's head, potentially strongly affecting data quality. To investigate how this eye-tracker slippage affects data quality, we designed experiments in which participants mimic behaviors that can cause a mobile eye tracker to move. Specifically, we investigated data quality when participants speak, make facial expressions, and move the eye tracker. Four head-worn eye-tracking setups were used: (i) Tobii Pro Glasses 2 in 50 Hz mode, (ii) SMI Eye Tracking Glasses 2.0 60 Hz, (iii) Pupil-Labs' Pupil in 3D mode, and (iv) Pupil-Labs' Pupil with the Grip gaze estimation algorithm as implemented in the EyeRecToo software. Our results show that whereas gaze estimates of the Tobii and Grip remained stable when the eye tracker moved, the other systems exhibited significant errors (0.8-3.1 • increase in gaze deviation over baseline) even for the small amounts of glasses movement that occurred during the speech and facial expressions tasks. We conclude that some of the tested eye-tracking setups may not be suitable for investigating gaze behavior when high accuracy is required, such as during face-to-face interaction scenarios. We recommend that users of mobile head-worn eye trackers perform similar tests with their setups to become aware of its characteristics. This will enable researchers to design experiments that are robust to the limitations of their particular eye-tracking setup. Keywords Head-mounted eye tracking • Wearable eye tracking • Mobile eye tracking • Eye movements • Natural behavior • Data quality Diederick C. Niehorster and Thiago Santini contributed equally and should be considered co-first authors.

show abstract

Section: Apparatusmentioning

confidence: 99%

The impact of slippage on the data quality of head-worn eye trackers

et al. 2020

View full text Add to dashboard Cite

show abstract

“…All are forms of learning. stereotypical behaviours (albeit with some inter-individual variation) during routine driving (for illustrative examples see Lappi, Rinkkala & Pekkanen, 2017). It seems that to steer smoothly drivers usually employ guiding fixations (GF): fixations directed about 1-2s ahead (Land, 1992;Land & Lee, 1994;Lappi, Lehtonen, Pekkanen & Itkonen, 2013;Lehtonen et al, 2014).…”

Section: Gaze and Steering Coordinationmentioning

confidence: 99%

“…During routine steering gaze patterns are very active, with a move-dwell-move pattern occurring 2-3 times per second (Wilkie et al, 2010;Lappi et al, 2017;Ahissar & Assa, 2016). Whilst it appears that gaze behaviours by themselves are important, the interplay between steering and gaze control is more nuanced than simply needing to look ahead to observe the scene features relevant for determining the upcoming steering requirements.…”

Section: Gaze and Steering Coordinationmentioning

confidence: 99%

“…intermittently without a major impact upon performance (Senders, 1967;Horrey & Wickens, 2007; Pekkanen et al, 2018 E will be intermittency in the gaze input due to gaze polling behaviours (Lappi et al, 2017). Also, recent modelling advances suggest that intermittent control could be a fundamental property of steering (Markkula et al, 2017).…”

Section: An Operational Control Loop That Reflects the Perceptual-motmentioning

confidence: 99%

See 1 more Smart Citation

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

Mole

Lappi

Giles³

et al. 2019

Hum Factors

Self Cite

View full text Add to dashboard Cite

Objective: To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to automated vehicle (AV)–human transitions. Background: Manual control of vehicles is made possible by the coordination of perceptual-motor behaviors (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood. Method: We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarized as a perceptual-motor loop that requires (a) calibration and (b) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework. Results: The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored. Conclusion: Modeling the coordination of gaze and steering and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving. Application: This conclusion poses a challenge for future research on AV-human transitions. Future studies need to provide an understanding of human behavior that will be sufficient to capture the essential characteristics of drivers reengaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering and potential routes to improving manual control recovery.

show abstract

“…However, driver eye movement studies have shown that drivers do not fixate only on this point, but also scan areas farther up the road in a controlled pattern [17]. A particular area of interest is the far road triangle [18], comprised of the TP, the Occlusion Point (OP), which is the farthest point of the road that is not blocked by obstacles in the field of vision, and the point where the driver's line of vision through the TP intersects the opposite lane edge, the Extended Tangent Point (ETP).…”

Section: Introductionmentioning

confidence: 99%

A Perceptually Inspired Driver Model for Speed Control in Curves

Gruppelaar

Paassen

Mulder

et al. 2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

View full text Add to dashboard Cite

Understanding speed control in driving is important for analysis of road geometry and for the development of driver support assistance devices. Current models for speed selection are primarily based on the relation between road geometry and observed speeds. This study proposes a more detailed model that relates individual speed control to accelerator and brake pedal, based on perception of the visual scene as captured by the Extended Tangent Point (ETP). We investigated the potential of the the Time to ETP (TETP) as input for accelerator and brake pedal control. Based on observations from driving studies, we propose a model, and tuning rules to adjust the model parameters to observed behavior. A simulator experiment showed that, after individualization of the thresholds using a binary classification method, the model is capable of accurately capturing individual speed adaptation of 15 drivers on single lane roads with multiple curves.

show abstract

Systematic Observation of an Expert Driver's Gaze Strategy—An On-Road Case Study

Cited by 51 publications

References 72 publications

The impact of slippage on the data quality of head-worn eye trackers

The impact of slippage on the data quality of head-worn eye trackers

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

A Perceptually Inspired Driver Model for Speed Control in Curves

Contact Info

Product

Resources

About