Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming

Xue, Qingwu; Markkula, Gustav; Yan, Xuedong; Merat, Natasha

doi:10.1016/j.aap.2018.06.006

Cited by 52 publications

(65 citation statements)

References 45 publications

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“…where G is the kinematic motor-primitive function present in the Brake pedal control generation part. In the present implementation, G is defined as in (12) to (15) with the brake-deflection target T set to 1. The G function generates a continuous control signal, given the brake-pedal target, whenever a brake-pedal adjustment is initiated.…”

Section: A: Excitatory Branchmentioning

confidence: 99%

“…Our implementation used the MATLAB ode45 function to solve differential equations, reducing execution time. The G function is a set of differential equations defined by (12) to (15) (using the same variable naming used by Dégallier Rochat [19]). The set of equations models one agonist muscle and one antagonist muscle (i = 1 and i = 2, respectively).…”

Section: D: Brake-pedal Controlmentioning

confidence: 99%

“…The choice of perceptual cues is crucial to make the model comparable to human control. In previous implementations (i.e., rear-end scenarios [9], [12]), the looming cue was used as input to the model. Similarly, our implementation also uses looming as an excitatory cue which elicits the need for braking.…”

Section: Introductionmentioning

confidence: 99%

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A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

2020

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The number of cyclists fatally struck when crossing a driver's travel path at an unsignalised intersection has been stable in recent years, indicating that more effort should be made to improve safety in this specific conflict scenario. The most recent safety systems help drivers avoid collisions with cyclists, but improving cyclist safety further requires resolving challenges unique to bicycles and cyclists. In this paper we propose a predictive computational model of driver behaviour in the intersection scenario. Although a handful of studies have focused on describing driver behaviour in this scenario, no computational model that can predict driver control can be found in the literature. The proposed model is based on a biofidelic human sensorimotor-control modelling framework. Two visual cues were used: 1) optical longitudinal looming and 2) projected post-encroachment time between the bicycle and the car. The model was optimised using data from a test-track study in which participants were asked to drive through an intersection where a cyclist would cross their travel path. The performances of the model were evaluated by comparing the simulated driver-control process with the observed control behaviour for each trial using a leave-one-out cross-validation process. The results show that the model performed rather well, reproducing braking controls and kinematics that were similar to the observations. The extent to which the model could be used by safety systems' threatassessment algorithms is discussed. Future research to improve the model's performances is suggested.

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Section: A: Excitatory Branchmentioning

confidence: 99%

Section: D: Brake-pedal Controlmentioning

confidence: 99%

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A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

2020

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“…Yet many sensorimotor decisions take place in the context of a continuous stream of varying sensory information. Models with variable drift rate, which we will refer to here as variable-drift diffusion models (VDDMs), have been successful in the vehicle driving context, accounting well for driver brake responses to the time varying visual looming of an approaching vehicle (Xue, Markkula, Yan, & Merat, 2018) as well as for steering responses during lanekeeping (Markkula, Boer, Romano, & Merat, 2018).…”

Section: Introductionmentioning

confidence: 99%

At the Zebra Crossing: Modelling Complex Decision Processes with Variable-Drift Diffusion Models

Giles¹,

Markkula²,

Pekkanen³

et al. 2019

Preprint

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Drift diffusion (or evidence accumulation) models have found widespread use in the modelling of simple decision tasks. Extensions of these models, in which the model’s instantaneous drift rate is not fixed but instead allowed to vary over time as a function of a stream of perceptual inputs, have allowed these models to account for more complex sensorimotor decision tasks. However, many real-world tasks seemingly rely on a myriad of even more complex underlying processes. One interesting example is the task of deciding whether to cross a road with an approaching vehicle. This action decision seemingly depends on sensory information both about own affordances (whether one can make it across before the vehicle) and action intention of others (whether the vehicle is yielding to oneself). Here, we compared three extensions of a standard drift diffusion model, with regards to their ability to capture timing of pedestrian crossing decisions in a virtual reality environment. We find that a single variable-drift diffusion model (S-VDDM) in which the varying drift rate is determined by visual quantities describing vehicle approach and deceleration, saturated at an upper and lower bound, can explain multimodal distributions of crossing times well across a broad range vehicle approach scenarios. More complex models, which attempt to partition the final crossing decision into constituent perceptual decisions, improve the fit to the human data but further work is needed before firm conclusions can be drawn from this finding.

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“…Third, we show that established evidence accumulation models of decision-making can be extended beyond typical laboratory paradigms with static or intermittently changing abstract stimuli, to a task with clear real-world relevance, and continuously time-varying sensory evidence. We and others have reported that evidence accumulation models show promise for modelling decisions in real-world tasks, e.g., when to apply brakes in response to a developing collision threat [52], [53], [61], or on whether and when to cross a road with oncoming traffic [62], [63]. However, in these types of naturalistic tasks it has not been possible to fit full response time probability distributions per participant, a minimum expectation in evidence accumulation modelling of more typical, abstract laboratory tasks.…”

Section: Discussionmentioning

confidence: 99%

Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

Markkula¹,

Uludağ²,

Wilkie³

et al. 2020

Preprint

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Detection of impending collision is fundamental to many human activities, and is widely assumed to be limited by a ‘looming threshold’. Evidence accumulation models explain decision-making in abstract paradigms, but have not been shown to remain valid for continuously time-varying, ecologically relevant stimuli. Here, we record behavioural and EEG responses in a collision detection task, disprove the conventional looming threshold assumption, and instead provide stringent evidence for a looming accumulation model. Generalising existing model assumptions from stationary to time-varying evidence, we show that our model accounts for previously unexplained observations and full distributions of detection. We replicate a centroparietal pre-decision positivity in scalp potentials, and show that our model explains its onset rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previous paradigms. Our findings illustrate the value of connecting basic and applied research on human behaviour.

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Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming

Cited by 52 publications

References 45 publications

A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

At the Zebra Crossing: Modelling Complex Decision Processes with Variable-Drift Diffusion Models

Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

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