Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 363-368 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781665453653 |
| ISBN (Print) | 978-1-6654-5366-0 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 21st IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022 - Singapore, Singapur Dauer: 17 Okt. 2022 → 21 Okt. 2022 |
Publikationsreihe
| Name | IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022 |
|---|---|
| ISSN (Print) | 2771-1102 |
| ISSN (elektronisch) | 2771-1110 |
Abstract
Participating in urban traffic is inherently risky for humans. There-fore, in psychology, behavioural studies have been using Virtual Reality (VR) to simulate and experiment with human behaviour. Safety critical interactions (e.g. conflict, collision or near collision) can be captured from the motion trajectories. However, the motion data in virtual settings is influenced by the modelling software used to create the virtual world, which might fail to capture one-to-one interactions accurately (such as interactions between pedestrians and cyclists in mixed traffic). Our system paper proposes a Pedestrian-in-the-Loop (PIL) Mixed Reality (MR) framework, where mobile virtual cyclist avatars co-exist with humans in a real-world outdoor space. Such a setting can be used to study a pedestrian subject, both viewing and interacting with moving holograms of cyclists in real traffic. The novelty of our approach is modelling virtual avatars as cognitive agents. To achieve this, we integrate agent-based models so that the virtual avatar can sense the environment and interact with the real user participating in the experiments. We demonstrate that this approach could contribute to effectively studying of pedestrian interactions. We also perform an evaluation to quantify the amount of trajectory error for our outdoor framework. For this, we compare the position data of a subject during an experiment to a proven benchmark for indoor motion capture. Additionally, an application of using the framework to demonstrate pedestrian dominance is presented.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Medientechnik
- Mathematik (insg.)
- Modellierung und Simulation
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- Apa
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- BibTex
- RIS
2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022. Institute of Electrical and Electronics Engineers Inc., 2022. S. 363-368 (IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Mixed Reality Agent-Based Framework for Pedestrian-Cyclist Interaction
AU - Kamalasanan, Vinu
AU - Mukbil, Awad
AU - Sester, Monika
AU - Muller, Jorg P.
N1 - Funding Information: This research is funded by the German Research Foundation (DFG) through the Research Training Group SocialCars (GRK 1931).
PY - 2022
Y1 - 2022
N2 - Participating in urban traffic is inherently risky for humans. There-fore, in psychology, behavioural studies have been using Virtual Reality (VR) to simulate and experiment with human behaviour. Safety critical interactions (e.g. conflict, collision or near collision) can be captured from the motion trajectories. However, the motion data in virtual settings is influenced by the modelling software used to create the virtual world, which might fail to capture one-to-one interactions accurately (such as interactions between pedestrians and cyclists in mixed traffic). Our system paper proposes a Pedestrian-in-the-Loop (PIL) Mixed Reality (MR) framework, where mobile virtual cyclist avatars co-exist with humans in a real-world outdoor space. Such a setting can be used to study a pedestrian subject, both viewing and interacting with moving holograms of cyclists in real traffic. The novelty of our approach is modelling virtual avatars as cognitive agents. To achieve this, we integrate agent-based models so that the virtual avatar can sense the environment and interact with the real user participating in the experiments. We demonstrate that this approach could contribute to effectively studying of pedestrian interactions. We also perform an evaluation to quantify the amount of trajectory error for our outdoor framework. For this, we compare the position data of a subject during an experiment to a proven benchmark for indoor motion capture. Additionally, an application of using the framework to demonstrate pedestrian dominance is presented.
AB - Participating in urban traffic is inherently risky for humans. There-fore, in psychology, behavioural studies have been using Virtual Reality (VR) to simulate and experiment with human behaviour. Safety critical interactions (e.g. conflict, collision or near collision) can be captured from the motion trajectories. However, the motion data in virtual settings is influenced by the modelling software used to create the virtual world, which might fail to capture one-to-one interactions accurately (such as interactions between pedestrians and cyclists in mixed traffic). Our system paper proposes a Pedestrian-in-the-Loop (PIL) Mixed Reality (MR) framework, where mobile virtual cyclist avatars co-exist with humans in a real-world outdoor space. Such a setting can be used to study a pedestrian subject, both viewing and interacting with moving holograms of cyclists in real traffic. The novelty of our approach is modelling virtual avatars as cognitive agents. To achieve this, we integrate agent-based models so that the virtual avatar can sense the environment and interact with the real user participating in the experiments. We demonstrate that this approach could contribute to effectively studying of pedestrian interactions. We also perform an evaluation to quantify the amount of trajectory error for our outdoor framework. For this, we compare the position data of a subject during an experiment to a proven benchmark for indoor motion capture. Additionally, an application of using the framework to demonstrate pedestrian dominance is presented.
KW - Behaviour
KW - Interaction modelling-shared spaces
KW - Mixed Reality
KW - Pedestrian in Loop
KW - safety
KW - Simulations
UR - http://www.scopus.com/inward/record.url?scp=85146050660&partnerID=8YFLogxK
U2 - 10.1109/ISMAR-Adjunct57072.2022.00079
DO - 10.1109/ISMAR-Adjunct57072.2022.00079
M3 - Conference contribution
AN - SCOPUS:85146050660
SN - 978-1-6654-5366-0
T3 - IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022
SP - 363
EP - 368
BT - 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022
Y2 - 17 October 2022 through 21 October 2022
ER -