The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
I Drive - You Trust: Explaining Driving Behavior Of Autonomous Cars
Abstract
Driving in autonomous cars requires trust, especially in case of unexpected driving behavior of the vehicle. This work evaluates mental models that experts and non-expert users have of autonomous driving to provide an explanation of the vehicle's past driving behavior. We identified a target mental model that enhances the user's mental model by adding key components from the mental model experts have. To construct this target mental model and to evaluate a prototype of an explanation visualization we conducted interviews (N=8) and a user study (N=16). The explanation consists of abstract visualizations of different elements, representing the autonomous system's components. We explore the relevance of the explanation's individual elements and their influence on the user's situation awareness. The results show that displaying the detected objects and their predicted motion was most important to understand a situation. After seeing the explanation, the user's level of situation awareness increased significantly.
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Telematics offers the potential to protect reputation systems from manipulation and to stabilize user trust in evaluations and transactions.
“This possibility offers the potential to protect reputation systems from manipulation and to stabilize user trust in evaluations and transactions (Olakanmi and Oluwaseun, 2018; Ribeiro et al., 2016; Wiegand et al., 2019; Stevens and Bossauer, 2020).”
From Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems (2024) · cites this paper as Wiegand et al., 2019
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Cites
Why and why not explanations improve the intelligibility of context-aware intelligent systems
Lim, B. Y., Dey, A. K., & Avrahami, D. (2009) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1519023 · Cited by 552 · cited 1×
“It calibrates trust in the automation algorithm and can enhance the take over behavior [9].”
BackgroundThe effects of transparency on trust in and acceptance of a content-based art recommender
Cramer, H., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., & Wielinga, B. (2008) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-008-9051-3 · Cited by 448
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Research works printed but not linked: 7
- Mariusz Bojarski , Philip Yeres , Anna Choromanska , Krzysztof Choromanski , Bernhard Firner , Lawrence Jackel , and Urs Muller . 2017. Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car. (April 2017 ). arXiv:cs.CV/1704.07911 Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence Jackel, and Urs Muller. 2017. Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car. (April 2017). arXiv:cs.CV/1704.07911
- 10.1145/3172944.3172961 · DOI · Paper page
- Mica R Endsley . 2017. Toward a theory of situation awareness in dynamic systems . In Situational Awareness . Routledge , 9--42. Mica R Endsley. 2017. Toward a theory of situation awareness in dynamic systems. In Situational Awareness. Routledge, 9--42.
- 10.1007/978-3-319-96071-5_32 · DOI
- David Gunning . 2017. Explainable artificial intelligence (xai) . Defense Advanced Research Projects Agency (DARPA) , nd Web ( 2017 ). David Gunning. 2017. Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web (2017).
- 10.1007/s12008-014-0227-2 · DOI
- Nava Tintarev and Judith Masthoff . 2011. Designing and evaluating explanations for recommender systems . In Recommender systems handbook . Springer , 479--510. Nava Tintarev and Judith Masthoff. 2011. Designing and evaluating explanations for recommender systems. In Recommender systems handbook. Springer, 479--510.
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Mental Models of AI Agents in a Cooperative Game Setting
Gero, K. I., Ashktorab, Z., Dugan, C., Pan, Q., Johnson, J., Geyer, W., Ruiz, M., Miller, S., Millen, D. R., Campbell, M., et al. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376316 · Cited by 121
Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems
Neifer, T., Bossauer, P., Pakusch, C., Boehm, L., & Lawo, D. (2024) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-022-09461-4 · Cited by 6
“The relevance of comprehensible systems can be seen in the increasing amount of work in the area of system intelligibility and Explainable AI (Abdul et al., 2018; Madumal et al., 2018; Strobel, 2018; Wiegand et al., 2019).”
Metadata
- Authors
- Wiegand, G., Schmidmaier, M., Weber, T., Liu, Y., & Hussmann, H.
- Affiliations
- Fortiss (DE)
- Ludwig-Maximilians-Universität München (DE)
- Venue
- Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2019-05-02
- Pages
- 1-6
- OpenAlex
- W2942678267
- Cited by
- 54
- References
- 2
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 9 · 2022: 10 · 2021: 11 · 2020: 8 · 2019: 1
- FWCI
- 12.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- autonomous driving, trust in autonomous vehicles, mental models, explainable AI, explanation visualization, situational awareness, human-vehicle interaction, object detection, motion prediction
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