What Preview Elements do Drivers Need?

Boer, Erwin R.

doi:10.1016/j.ifacol.2016.10.469

Cited by 13 publications

(26 citation statements)

References 7 publications

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“…In Donges (1978) the two-levels refer to modes of control (in the original conception anticipatory was 'feedforward' and compensatory was 'feedback'), rather than referring to the information obtained from specific points in the scene. The two levels have since been linked to specific near and far portions of the scene (Land & Horwood, 1995) and further explicitly implemented as two points (Salvucci & Gray, 2004; see also Boer, 2016). Therefore, for most discussions two-levels and two-points can be considered synonymous, but there is a distinction to be drawn whereby the two point model remains only one possible implementation of the twolevel concepts.…”

Section: Discussionmentioning

confidence: 99%

“…The behavioral relationship is assumed to be a basic control model which is divided into guidance control using far vision (Figure 1, Guidance) and compensatory control using near vision (Figure 1, Compensatory). Whereas the weightings of the components displayed in Figure 1 will vary depending on the nature of the steering task, the general principles appear to be well supported and act as the basis of many current steering models (e.g., Sentouh, Chevrel, Mars, & Claveau, 2009;Saleh, Chevrel, Mars, Lafay, & Claveau, 2011;Boer, 2016;You & Tsiotras, 2016;Markkula, Benderius, & Wahde, 2014;Mars & Chevrel, 2017).Given the widespread prevalence of such two-point steering models it is worth noting that the precise sources of near and far information are often only weakly specified. Road environments are rich sources of information, containing a large set of features from near and far regions that could contribute to estimates of position in lane and the future steering requirements.…”

Section: Introductionmentioning

confidence: 99%

“…(which provides a preview of future changes in direction), and a near point (which indicates current position-in-lane; Donges, 1978;Salvucci & Gray, 2004;Boer, 2016). The key principles of two-point control models have been tested by examining driver behavior when far (preview) or near (position-in-lane) information has been selectively removed.…”

Section: Introductionmentioning

confidence: 99%

“…In theoretical accounts it is often assumed that angular inputs would be obtained from roadedges; however, the precise mechanisms for extracting this information are unclear. Computational driver models during curve following tend to use angular inputs between the direction of travel of the vehicle and points on the road center rather than signals obtained directly from road-edges (Salvucci & Gray, 2004;Boer, 2016;You & Tsiotras, 2016;Markkula et al, 2014;Mars & Chevral, 2017, although in some cases the near point has been implemented as dependent on road-edge information; Kountouriotis, Floyd, Gardner, Merat, & Wilkie, 2012). These accounts do not disentangle use of road-edge information from the other perceptual inputs that are available when looking where you are going (such as gaze direction 1 or retinal flow; cf.…”

Section: Introductionmentioning

confidence: 99%

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Steering bends and changing lanes: The impact of optic flow and road edges on two point steering control

et al. 2018

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Successful driving involves steering corrections that respond to immediate positional errors while also anticipating upcoming changes to the road layout ahead. In popular steering models these tasks are often treated as separate functions using two points: the near region for correcting current errors, and the far region for anticipating future steering requirements. Whereas two-point control models can capture many aspects of driver behavior, the nature of perceptual inputs to these two "points" remains unclear. Inspired by experiments that solely focused on road-edge information (Land & Horwood, 1995), two-point models have tended to ignore the role of optic flow during steering control. There is recent evidence demonstrating that optic flow should be considered within two-point control steering models (Mole, Kountouriotis, Billington, & Wilkie, 2016). To examine the impact of optic flow and road edges on two-point steering control we used a driving simulator to selectively and systematically manipulate these components. We removed flow and/or road-edge information from near or far regions of the scene, and examined how behaviors changed when steering along roads where the utility of far-road information varied. While steering behaviors were strongly influenced by the road-edges, there were also clear contributions of optic flow to steering responses. The patterns of steering were not consistent with optic flow simply feeding into two-point control; rather, the global optic flow field appeared to support effective steering responses across the time-course of each trajectory.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Steering bends and changing lanes: The impact of optic flow and road edges on two point steering control

et al. 2018

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show abstract

“…It is worth mentioning that because these perception-action loops are modelled on successful human steering behaviours in laboratory steering tasks, they tend to describe well-calibrated behaviors (see e.g. Wilkie et al, 2008;Mars & Chevral, 2017;Salvucci & Gray, 2004;Boer, 2016), however the way that the human has become calibrated, and how calibration can adapt in more dynamically complex and labile environments, tends not to be explicitly addressed.…”

Section: Gaze and Steering Coordinationmentioning

confidence: 99%

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

Mole

Lappi

Giles³

et al. 2019

Hum Factors

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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.

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Modelling visual-vestibular integration and behavioural adaptation in the driving simulator

Markkula

Romano

Waldram

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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It is well established that not only vision but also other sensory modalities affect drivers' control of their vehicles, and that drivers adapt over time to persistent changes in sensory cues (for example in driving simulators), but the mechanisms underlying these behavioural phenomena are poorly understood. Here, we consider the existing literature on how driver steering in slalom tasks is affected by the down-scaling of vestibular cues, and propose a driver model that can explain the empirically observed effects, namely: decreased task performance and increased steering effort during initial exposure, followed by a partial reversal of these effects as task exposure is prolonged. Unexpectedly, the model also reproduced another empirical finding: a local optimum for motion down-scaling, where path-tracking is better than when one-to-one motion cues are available. Overall, the results imply that: (1) drivers make direct use of vestibular information as part of determining appropriate steering, and (2) motion down-scaling causes a yaw rate underestimation phenomenon, where drivers behave as if the simulated vehicle is rotating more slowly than it is. However, (3) in the slalom task, a certain degree of such yaw rate underestimation is beneficial to path tracking performance. Furthermore, (4) behavioural adaptation, as empirically observed in slalom tasks, may occur due to (a) down-weighting of vestibular cues, and/or (b) increased sensitivity to control errors, in determining when to adjust steering and by how much, but (c) seemingly not in the form of a full compensatory rescaling of the received vestibular input. The analyses presented here provide new insights and hypotheses about simulator driving, and the developed models can be used to support research on multisensory integration and behavioural adaptation in both driving and other task domains.

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What Preview Elements do Drivers Need?

Cited by 13 publications

References 7 publications

Steering bends and changing lanes: The impact of optic flow and road edges on two point steering control

Steering bends and changing lanes: The impact of optic flow and road edges on two point steering control

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

Modelling visual-vestibular integration and behavioural adaptation in the driving simulator

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