Time to Arrival as Predictor for Uncertainty and Cooperative Driving Decisions in Highly Automated Driving

Miller, Linda; Leitner, Jasmin; Kraus, Johannes; Lee, Jieun; Daimon, Tatsuru; Kitazaki, Satoshi; Baumann, Martin

doi:10.1109/iv51971.2022.9827416

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…While closely related, the concepts of confidence and certainty are however fundamentally distinct, with known dissociations between them [29,51]. Our findings are generally consistent with the patterns of certainty judgments observed in previous studies of driver behavior [25][26][27][28]50], although the manner in which (un)certainty is analyzed in the literature complicates a direct comparison. Specifically, in this line of research, (un)certainty has been analyzed as a function of kinematic variables (such as TTA) disregarding the decision outcome.…”

Section: Empirical Studies Of Confidencesupporting

confidence: 87%

“…Among the existing applied work on driver decision making, the studies most related to this paper are the ones that studied drivers' certainty (or, alternatively, uncertainty) in gap acceptance decisions in lane changes [25,26,50], narrow passages [28], and overtaking [27]. While closely related, the concepts of confidence and certainty are however fundamentally distinct, with known dissociations between them [29,51].…”

Section: Empirical Studies Of Confidencementioning

confidence: 99%

“…In the context of lane-change decisions, driver uncertainty judgments (defined as "driver's difficulty to make appropriate decisions of either changing the lane or not in a given lane change situation") have been shown to depend on kinematics of vehicles in the traffic scene [25,26]. Uncertainty judgments have also been investigated in overtaking decisions [27] and interactions at narrow passages [28]. However, uncertainty judgments in traffic decisions have so far been analyzed disregarding decision outcomes, obscuring the relationship between these judgments and task parameters.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Empirical Studies Of Confidencesupporting

confidence: 87%

Section: Empirical Studies Of Confidencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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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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Narrow passage interactions: A UK-based exploratory survey study to identify factors affecting driver decision-making

Youssef,

Plant,

Waterson

2024

Transportation Research Part F: Traffic Psychology and Behaviou

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Time to Arrival as Predictor for Uncertainty and Cooperative Driving Decisions in Highly Automated Driving

Cited by 5 publications

References 29 publications

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

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

Narrow passage interactions: A UK-based exploratory survey study to identify factors affecting driver decision-making

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