Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios

Abstract

Abstract:The recently proposed cognitive control hypothesis suggests that the performance of cognitively loading but nonvisual tasks such as cell phone conversation selectively impairs driving tasks that rely on top-down cognitive control while leaving automatized driving tasks unaffected. This idea is strongly supported by the existing experimental literature and we have previously outlined a conceptual model to account for the key underlying mechanisms. The present paper presents a mechanistically explicit account of the cognitive control hypothesis in terms of a computational simulation model. More specifically, it is shown how this model offers a straightforward explanation for why the effect of cognitive load on brake response time reported in experimental lead vehicle braking studies depends strongly on scenario kinematics, more specifically the initial time headway. It is demonstrated that this relatively simple model can be fitted to empirical data obtained from an existing meta-analysis on existing lead vehicle braking studies.

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

“…Importantly, evidence accumulation models capture the phenomena of the kinematics dependence of takeover time and the variability of response times increasing with average response times, as observed both in manual and automated driving (Markkula, Engström, et al, 2016; Zhang et al, 2018). Evidence accumulation models have been extended to include the effects of cognitive distraction (Engström, Markkula, Xue, & Merat, 2018). In the extended model, cognitive load slows the evidence accumulation process, leading to prolonged reaction times.…”

The Role of Situation Criticality in Affecting the Effect of Cognitive Load on Drivers’ Brake responses: A Driving Simulator Based Study

“…erefore, only 84 recordings were included for the first braking scenario and 132 recordings were kept for the second braking scenario. As a follow-up study of Engström et al [33], recordings within each drive were sorted into nine groups regarding their initial time headways (ITHWs). Drivers' mean brake response times (BRTs) within each ITHW group were calculated as the observations to fit the accumulator model.…”

“…However, why does this effect depend on initial time headway and how drivers perceive time headway information and use it to determine when to apply braking were still not clear. To further explain the key underlying mechanisms, Engström et al [33] outlined a mechanistic model with the data from the meta-analysis. Visual looming, which is produced by the sudden braked lead vehicle moving towards the subject was adopted in this model to measure situation criticality.…”

“…To further test the relation between situation criticality and the effect of cognitive load on drivers' brake responses and better understand the underlying mechanism on drivers' brake responses, data collected from a driving simulator study was fitted in the accumulator model in this paper. Comparing with the study of Engström et al [33], this paper developed in three principal ways. (1) Participants' ages were restricted in this experiment to eliminate the possible effect of age on drivers' responses.…”

“…(1) Participants' ages were restricted in this experiment to eliminate the possible effect of age on drivers' responses. (2) ree phone types and two kinds of conversation complexity were included in the experiment to examine whether the proposed model in Engström et al [33] could provide a good prediction for different combinations of cognitive load levels. (3) e lead vehicle would brake twice in each conversation condition, with the first brake assumed to be less critical and the second brake assumed to be more critical.…”