Response times in drivers' gap acceptance decisions during overtaking

Sevenster, Annemartijne; Farah, Haneen; Abbink, David A.; Zgonnikov, Arkady

doi:10.1016/j.trf.2023.03.003

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…Our study contributes to the emerging body of literature demonstrating that timing of gap acceptance decisions depends on the characteristics of the gap size. Our results are consistent with the finding of Gonçalves et al (2022) that response times in accepted gaps decrease with distance (the effect that has also been reported for overtaking gap acceptance (Sevenster et al, 2023)). Furthermore, our data generalize the results of Gonçalves et al (2022) in multiple ways.…”

Section: Discussionsupporting

confidence: 93%

“…experienced decision difficulty and choice strategy (Luce, 1986). Previous studies of timing of gap acceptance decisions demonstrated that response times in accept decisions are faster than in reject decisions in left turn and overtaking decisions (Zgonnikov et al, 2023;Sevenster et al, 2023). This difference can be attributed to increased pressure to make the accept decision; otherwise the opportunity to take the gap would be gone.…”

Section: This Studymentioning

confidence: 95%

“…Duration of the gap acceptance process has also been shown to depend on the kinematics of the vehicles in the scene. This is the case in pedestrian crossing (Pekkanen et al, 2022), left turns across path (Zgonnikov et al, 2022(Zgonnikov et al, , 2023, and overtaking (Sevenster et al, 2023). However, response times during merging gap acceptance remain poorly understood.…”

Section: This Studymentioning

confidence: 99%

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Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Zgonnikov,

van Niekerk,

Eisma

et al. 2023

Preprint

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Merging onto a highway is a safety-critical task resulting in a large number of traffic accidents; fundamental research into merging behavior of human drivers can help reduce this toll. Two cognitive processes critical to merging, attention allocation and decision making, have been extensively studied in both natural human behavior in real traffic and simulated driving. However, the interaction between attention and decision making during merging remains poorly understood. To address this gap, here we investigated the relationship between kinematics of the merging scenario, gap acceptance decisions, response timing, and eye movements of drivers during merging. In a video-based experiment, our participants (N=24) repeatedly made gap acceptance decisions based on the information available in the front view and the side mirror while their response times and eye movements were recorded. We found that decisions to accept a gap were much faster than decisions to reject a gap: the dynamically decreasing gap demanded that participants accept gaps in a timely manner. Furthermore, merging decisions and their timing depended on the distance and time-to-arrival to the overtaking vehicle, but also on the time pressure imposed by the approaching end of the merging lane. Most importantly, the proportion of time participants spent looking at the rear-view mirror was associated with differences in the probability of accepting the gap, controlling for duration of the decision-making process. This effect was particularly pronounced in conditions with low time budgets, suggesting that appropriate glancing behavior can be critical for merging decisions under time pressure. Our results lay foundation for future efforts on cognitive modeling of the interplay between decision-making and attention processes during merging and can aid in understanding unsafe merging maneuvers related to improper attention distribution.

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Section: Discussionsupporting

confidence: 93%

Section: This Studymentioning

confidence: 95%

Section: This Studymentioning

confidence: 99%

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Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Zgonnikov,

van Niekerk,

Eisma

et al. 2023

Preprint

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“…We found a positive relation between response time on the one hand and the distance gap and time-to-arrival on the other hand. These findings are consistent with the previous literature that reported such positive relationships in similar decisions during left turns across path [22,23], overtaking [24,33], merging [47], and lane changing [26].…”

Section: Response Times In Gap Acceptance Decisionssupporting

confidence: 93%

“…We expected to observe a positive relation between the time-to-arrival and distance gap conditions and the probability of "go" decision (Hypothesis 1.1, H1.1), and a positive relation between the response time and the time-to-arrival (but not distance [22]) for both decisions (H1.2). These hypotheses are based on the previous studies of left-turn and overtaking gap acceptance decisions which found that the probability of accepting the gap as well as the response times are affected by the perceptual information capturing the time-to-arrival and distance gap [22,23,33].…”

Section: Decision Behavior and Response Timesmentioning

confidence: 99%

Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers

2022

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Objective We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Background Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Results The drivers’ probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. Conclusion Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. Application Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles.

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How Sure is the Driver? Modelling Drivers’ Confidence in Left-Turn Gap Acceptance Decisions

Bontje,

Zgonnikov

2024

Comput Brain Behav

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When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better understanding of the mechanisms responsible for these confidence judgments could provide novel insights into human behavior. However, so far confidence judgments have been mostly studied in simplistic laboratory tasks while little is known about confidence in naturalistic dynamic tasks such as driving. In this study, we made a first attempt of connecting fundamental research on confidence with naturalistic driver behavior. We investigated the confidence of drivers in left-turn gap acceptance decisions in a driver simulator experiment (N = 17). We found that confidence in these decisions depends on the size of the gap to the oncoming vehicle. Specifically, confidence increased with the gap size for trials in which the gap was accepted, and decreased with the gap size for rejected gaps. Similarly to more basic tasks, confidence was negatively related to the response times and correlated with action dynamics during decision execution. Finally, we found that confidence judgments can be captured with an extended dynamic drift–diffusion model. In the model, the drift rate of the evidence accumulator as well as the decision boundaries are functions of the gap size. Furthermore, we demonstrated that allowing for post-decision evidence accumulation in the model increases its ability to describe confidence judgments in rejected gap decisions. Overall, our study confirmed that principles known from fundamental confidence research extend to confidence judgments in dynamic decisions during a naturalistic task.

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Response times in drivers' gap acceptance decisions during overtaking

Cited by 9 publications

References 31 publications

Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers

How Sure is the Driver? Modelling Drivers’ Confidence in Left-Turn Gap Acceptance Decisions

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