Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions

Jami Pekkanen,Yee Mun Lee,Natasha Merat,Ruth Madigan,Oscar Terence Giles,Tatsuru Daimon,Gustav Markkula

doi:10.1007/s42113-021-00116-z

Abstract

Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency.

Highlights

IntroductionAs performed by individuals either alone or in concert with others, has been an object of scientific study for a long time (Gibson & Crooks, 1938), Existing approaches to computational modeling of road user behavior mirror the modeling paradigms in the Comput Brain Behav wider cognitive and behavioral sciences, including cognitive architectures (Salvucci, 2006), ecological psychology (Fajen, 2013), classical and optimal control theory (Plochl & Edelmann, 2007), rational decision-making (Choudhury et al, 2007), game theory (Elvik, 2014; Hoogendoorn & Bovy, 2003), as well as data-driven modeling using machine learning approaches (Behbahani et al, 2019; Ma et al, 2016)
One type of model that has been uncommon in road user modeling, but which has over recent decades become prominent in more basic psychology and cognitive neuroscience research, is drift diffusion, or evidence accumulation, models of decision-making
The nested model selection for distance coefficient βD and acceleration coefficient βτshows considerable improvement in the likelihood, especially when both parameters are free to vary, indicating that under the model vehicle distance and accleration cues had a significant impact on pedestrian crossing, beyond the impact of the pure time to arrival (TTA) cue

Summary

Introduction

As performed by individuals either alone or in concert with others, has been an object of scientific study for a long time (Gibson & Crooks, 1938), Existing approaches to computational modeling of road user behavior mirror the modeling paradigms in the Comput Brain Behav wider cognitive and behavioral sciences, including cognitive architectures (Salvucci, 2006), ecological psychology (Fajen, 2013), classical and optimal control theory (Plochl & Edelmann, 2007), rational decision-making (Choudhury et al, 2007), game theory (Elvik, 2014; Hoogendoorn & Bovy, 2003), as well as data-driven modeling using machine learning approaches (Behbahani et al, 2019; Ma et al, 2016). The saliency of the available evidence (for example, the movement coherence of a set of dots on a visual display, in a paradigm where the task is to judge the overall direction of dot motion) affects evidence accumulation rate and overall response times, and the noise in the accumulation process introduces variability, allowing these models to predict full distributions of choices made and the corresponding response times (Gold & Shadlen, 2007; Ratcliff et al, 2016) These general ideas can take a range of more specific computational forms, some of which explicitly leverage neuroscientic concepts and modeling components (Bogacz & Gurney, 2007; Purcell et al, 2010; Usher & McClelland, 2001; Wong & Wang, 2006), for example, inhibition between competing decisions, similar to lateral inhibition in the brain. It is less well known to what extent models of this nature can describe human decision-making well in more applied and embodied contexts, for example, relating to human sensorimotor control and movement in the real world

Results

Discussion

Conclusion