After you: Merging at Highway On-Ramps

Stoll, Tanja; Weihrauch, Lucas; Baumann, Martin

doi:10.1177/1071181320641266

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…However, only a very limited number of studies in a controlled environment (i.e., in a driving simulator) targeted interactions during merging (i.e., excluding studies of autonomous control strategies, gap acceptance, or traffic flow). Stoll et al investigated human decision-making in merging scenarios based on videos of a controlled simulation [19]. Participants had to select their preferred reaction (e.g., accelerate or decelerate) after watching videos of vehicles they were "interacting with".…”

Section: Introductionmentioning

confidence: 99%

Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?

Siebinga,

Zgonnikov,

Abbink

2024

IEEE Open J. Intell. Transp. Syst.

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Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on merging use naturalistic data. Although this provides insight into human gap acceptance and traffic flow effects, it obscures the operational inputs of interacting drivers. Besides that, researchers have no control over the vehicle kinematics (i.e., positions and velocities) at the start of the interactions. Therefore the relationship between initial kinematics and the outcome of the interaction is difficult to investigate. To address these gaps, we conducted an experiment in a coupled driving simulator with a simplified, top-down view, merging scenario with two vehicles. We found that kinematics can explain the outcome (i.e., which driver merges first) and the duration of the merging conflict. Furthermore, our results show that drivers use key decision moments combined with constant acceleration inputs (intermittent piecewise-constant control) during merging. This indicates that they do not continuously optimise their expected utility. Therefore, these results advocate the development of interaction models based on intermittent piecewise-constant control. We hope our work can contribute to this development and to the fundamental knowledge of interactive driver behaviour.

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Section: Introductionmentioning

confidence: 99%

Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?

Siebinga,

Zgonnikov,

Abbink

2024

IEEE Open J. Intell. Transp. Syst.

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“…The criticality of the situation and the scope of action, manipulated by the number of lanes or the available time for a driver to act, are context factors that influence driver's behavior in manual driving (Heesen et al, 2012;Stoll et al, 2020a;Stoll et al, 2020b) and takeover scenarios (Wu et al, 2019) as well as the acceptance of behavior choices of an automated vehicle (Stoll et al, 2021). With reducing the scope of action, the situation was perceived as more critical (Stoll et al, 2020b) and the acceptance of automated behavior increased (Stoll et al, 2021). When the criticality of the situation was higher, takeover time was shorter, and takeovers were more critical and unsafe, which was reflected in a shorter time to collision to the front vehicle (Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

Lanzer¹,

Stoll²,

Colley

et al. 2021

Front. Hum. Dyn.

Self Cite

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Automated driving in urban environments not only has the potential to improve traffic flow and heighten driver comfort but also to increase traffic safety, particularly for vulnerable road users such as pedestrians. For these benefits to take effect, drivers need to trust and use automated vehicles. This decision is influenced by both system and context factors. However, it is not yet clear how these factors interact with each other, especially for automated driving in city scenarios with crossing pedestrians. Therefore, we conducted an online experiment in which participants (N = 68) experienced short automated rides from the driver’s perspective through an urban environment. In each of the presented videos, a pedestrian crossed the street in front of the automated vehicle while system and context factors were varied: 1) the crossing pedestrian’s intention was either visualized correctly (as crossing) or incorrectly (visualization missing) by the automated vehicle (system factor), 2) the pedestrian was either distracted by using a smartphone while crossing or not (context factor), and 3) the scenario was either more or less complex depending on the number of other vehicles and pedestrians being present (context factor). In situations with a system malfunction where the crossing pedestrian’s intention was not visualized, participants perceived the situation as more critical, had less trust in the automated system, and a higher willingness to take over control regardless of any context factors. However, when the system worked correctly, the crossing pedestrian’s smartphone usage came into play, especially in the less complex scenario. Participants perceived situations with a distracted pedestrian as more critical, trusted the system less, indicated a higher willingness to take over control, and were more uncertain about their decision. As this study demonstrates the influence of distracted pedestrians, more research is needed on context factors and their inclusion in the design of interfaces to keep drivers informed during automated driving in urban environments.

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Measuring and Describing Cooperation Between Road Users—Results from CoMove

Quante,

Stoll,

Baumann

et al. 2024

Cooperatively Interacting Vehicles

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Safe and efficient traffic requires that road users interact and cooperate with each other. Especially in situations which are not explicitly regulated, and the right of way is not clearly defined, it is of great importance that road users are able to communicate their own intentions and understand the communication and cooperation behaviour of the other involved road users. When automated vehicles enter the current traffic system, their ability to fit into the system, that is their ability to communicate and cooperate, will determine their success. Therefore, the development of cooperatively interacting, automated vehicles requires detailed knowledge about human cooperation behaviour in traffic, which can only be obtained using appropriate methods and measures. By focusing on road narrowings and lane changing, this chapter gives an overview on how to measure cooperation between road users, considering methods for data collection, subjective and objective measures of cooperation as well as behaviour modeling, to support the systematic research on cooperation in road traffic. This overview is extended by findings from studies conducted within CoInCar, including results on factors influencing human behaviour in cooperative situations, either in a manual or an automated setting, and initial findings from modeling the cognitive processes underlying cooperative driving behaviour.

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After you: Merging at Highway On-Ramps

Cited by 6 publications

References 15 publications

Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?

Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

Measuring and Describing Cooperation Between Road Users—Results from CoMove

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