When cooperation is needed: the effect of spatial and time distance and criticality on willingness to cooperate

Stoll, Tanja; Müller, Fabian; Baumann, Martin

doi:10.1007/s10111-018-0523-x

Cited by 19 publications

(11 citation statements)

References 15 publications

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“…Simulated videos were chosen as they are a suitable tool to allow good controllability and standardization of scenarios for all participants. Videos of real or simulated traffic scenes are a commonly used method in traffic psychology (Borowsky and Oron-Gilad, 2013;Stoll et al, 2019;Wintersberger et al, 2020;Colley et al, 2021) that have shown similar results to studies in real traffic (Hughes and Cole, 1986). Compared to real-world traffic videos, simulated videos do not have privacy issues that require pixelation of pedestrian faces or complete recreation of scenes with instructed pedestrians.…”

Section: Methodsmentioning

confidence: 92%

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.

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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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Section: Methodsmentioning

confidence: 92%

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.

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“…The velocity of the observed car was kept constant to exclude the effects of varying dynamics. However, dynamic information is known to enhance anticipation [29] and affect behavioural outcomes in lane change scenarios [43]. Thus, we will strive for including dynamic anticipatory cues in future investigations.…”

Section: Discussionmentioning

confidence: 99%

Look ahead: understanding cognitive anticipatory processes based on situational characteristics in dynamic traffic situations

Mühl

Stoll

Baumann

2020

IET Intelligent Transport Systems

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“…The goal is to find strategies for the agents to produce minimal global costs during a scenario, and one approach is to implement cooperative strategies that lead to cooperative maneuvers, i.e., the agents collectively seek an interaction that lowers global costs and supports fairness. Since there are unlimited snapshots, each of which can lead to unlimited maneuvers, the number of cooperative scenarios is enormous [15,16].…”

Section: Cooperation In Road Trafficmentioning

confidence: 99%

The Need for Cooperative Automated Driving

et al. 2020

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In this paper we describe cooperation and social dilemmas in multiagent systems, with an analogy applied to road traffic. Cooperative human drivers, based on their perception of trust and fairness, find efficient solutions for such dilemmas. In the development of automated vehicles (AVs) it is therefore important to ensure that this cooperative ability is maintained even without a human driver. Therefore, the topic of cooperative intelligent transport systems (C-ITSs) is discussed in detail and different characteristics of cooperation and their implementation are derived. Further, three planning levels with the corresponding communication techniques are discussed and several methods for maneuver planning are listed. All in all, we hope that this paper will allow us to better classify different cooperative scenarios, develop novel approaches for cooperative AVs (CAVs), and emphasize the need for cooperative driving.

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When cooperation is needed: the effect of spatial and time distance and criticality on willingness to cooperate

Cited by 19 publications

References 15 publications

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

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

Look ahead: understanding cognitive anticipatory processes based on situational characteristics in dynamic traffic situations

The Need for Cooperative Automated Driving

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