Modeling road user response timing in naturalistic settings: a surprise-based framework

Abstract

There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. Our main contribution is the development of a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation-dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.

Beyond simple laboratory studies: developing sophisticated models to study rich behavior

“…Except in limited conditions, considering the decisions of drivers and navigators as "responses" to external stimuli that have some clear onset ignores the realities of temporal sensory dynamics. Prior work has proposed the use of models that keep track of the continuous estimation of affordances and how they change over time to address such limitations [6,20].…”

“…Similar forms of behavior are difficult to fit in the classical structure of laboratory studies, which benefit from measuring activity across repetitions of similar discrete "trials" and controlled manipulations of stimuli in "blocks". For example, when studying how a car driver enters a new lane, it is not straightforward to define when the "trial" starts and what is the "stimulus onset", since the action under study (entering a lane) is embedded in a continuous ongoing activity of "car driving" [6,7]. Other challenges include the difficulty of characterizing the high interand intra-individual variability of behavior that might emerge in ecologically valid conditions and the necessity of disentangling causes and consequences within a convoluted network of interactions between multiple agents and the environment.…”

Is trust a belief, observation, or state: Results from an active inference analysis of driver-automation transitions of control

“…Despite their broad applicability, current driver process models are limited by their ability to model internal driver decision making processes, i.e., cognitive dynamics. Active inference theory provides a promising platform for modeling these driver cognitive dynamics (Engström et al, 2022;, however the relationship between modeled and measured cognitive dynamics is not well established. The goal of this work is to establish this relationship by fitting an active inference model and analyzing correlations between model parameters and subjective trust, situation awareness, fatigue, and demographics.…”