Insight into cooperation processes for traffic scenarios: modelling with naturalistic decision making

Imbsweiler, Jonas; Stoll, Tanja; Ruesch, Maureen; Baumann, Martin; Deml, Barbara

doi:10.1007/s10111-018-0518-7

Cited by 19 publications

(2 citation statements)

References 28 publications

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“…They concluded that a digital decision-making system could improve safety at junctions. Furthermore, V2V/V2I may imply that no visible communication between vehicles is needed anymore, as all traffic negotiations could be pre-arranged [59].…”

Section: Av Vs Avmentioning

confidence: 99%

Driving Style: How Should an Automated Vehicle Behave?

et al. 2019

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This article reports on a study to investigate how the driving behaviour of autonomous vehicles influences trust and acceptance. Two different designs were presented to two groups of participants (n = 22/21), using actual autonomously driving vehicles. The first was a vehicle programmed to drive similarly to a human, “peeking” when approaching road junctions as if it was looking before proceeding. The second design had a vehicle programmed to convey the impression that it was communicating with other vehicles and infrastructure and “knew” if the junction was clear so could proceed without ever stopping or slowing down. Results showed non-significant differences in trust between the two vehicle behaviours. However, there were significant increases in trust scores overall for both designs as the trials progressed. Post-interaction interviews indicated that there were pros and cons for both driving styles, and participants suggested which aspects of the driving styles could be improved. This paper presents user information recommendations for the design and programming of driving systems for autonomous vehicles, with the aim of improving their users’ trust and acceptance.

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Section: Av Vs Avmentioning

confidence: 99%

Driving Style: How Should an Automated Vehicle Behave?

et al. 2019

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“…Yet, these authors merely presented natural‐world examples for empirical support, and no explicit experimental studies were conducted in these original articles. A few real‐world applications somewhat relevant to classification decisions can be found in human‐factor literature, such as air traffic control (Bonaceto, Estes, Moertl, & Burns, 2005 ) and transportation safety (Imbsweiler, Stoll, Ruesch, Baumann, & Deml, 2018 ). Interestingly, researchers in forensic science also investigated how contextual, categorical information (e.g., demographic information) reported by forensic experts may prompt cognitive bias and consequently impact the decision‐making of jurors (Dror et al., 2021 ; Kukucka & Dror, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Integrating Categorization and Decision‐Making

2023

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Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were shown six faces varying in width, first asked to categorize the faces, and then decide a course of action for each face. Each experiment was designed to include three groups, and for each group, we manipulated the probabilistic contingencies between stimulus, category assignments, and decision consequences. For each group, each participant received three different sequences of category response, category feedback, decision response, and decision feedback. We found that participants were only partially responsive in the appropriate directions to the contingencies assigned to each group. Comparisons of results from different sequences provided evidence for empirical interference effects of categorization on decisions. The empirical interference effect is defined as the difference between the probability of taking a hostile action in decision-alone conditions and the total probability of taking a hostile action in categorization-decision conditions. To test competing accounts for multiple empirical results, including two-stage choice probabilities and empirical interference effects, we compared a quantum cognition model versus a two-stage exemplar categorization model at both aggregate and individual levels. Using a Bayesian information criterion, we found that the quantum model provided an overall better model fit than the exemplar model. Although both models predicted empirical interference effects, the exemplar model was able to generate probabilistic deviation by incorporating category information of the first stage into the feature representation of the Correspondence should be sent to Rong Zheng,

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