Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 |
| Herausgeber/-innen | Bo An, Amal El Fallah Seghrouchni, Gita Sukthankar |
| Erscheinungsort | Richland |
| Seiten | 1878-1880 |
| Seitenumfang | 3 |
| ISBN (elektronisch) | 9781450375184 |
| Publikationsstatus | Veröffentlicht - 13 Mai 2020 |
Abstract
Realistically modelling behaviour and interaction of mixed road users (pedestrians and vehicles) in shared spaces are challenging due to the heterogeneity of transport modes and the absence of classical traffic rules. Existing models have mostly used the expert-based approach, combining symbolic modelling and reasoning paradigm with the hand-crafted encoding of the decision logic. Recently, deep learning (DL) models have been largely used to predict trajectories based on e.g. video data. Studies comparing expert-based and DL-based micro-simulation of shared spaces concerning their accuracy are missing, and so are proven methodologies for combining these approaches into a single agent-based system. In this paper, we propose and compare an expert-based and a DL model and then combine them for trajectory prediction in shared spaces. Simulation results show the combined model to outperform both pure approaches in predicting realistic and collision-free trajectories.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Software
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. Hrsg. / Bo An; Amal El Fallah Seghrouchni; Gita Sukthankar. Richland, 2020. S. 1878-1880.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - An Agent-Based Model for Trajectory Modelling in Shared Spaces
T2 - A Combination of Expert-Based and Deep Learning Approaches
AU - Johora, Fatema T.
AU - Cheng, Hao
AU - Müller, Jörg P.
AU - Sester, Monika
N1 - Funding Information: This research is funded by the German Research Foundation (DFG) through the Research Training Group SocialCars (GRK 1931).
PY - 2020/5/13
Y1 - 2020/5/13
N2 - Realistically modelling behaviour and interaction of mixed road users (pedestrians and vehicles) in shared spaces are challenging due to the heterogeneity of transport modes and the absence of classical traffic rules. Existing models have mostly used the expert-based approach, combining symbolic modelling and reasoning paradigm with the hand-crafted encoding of the decision logic. Recently, deep learning (DL) models have been largely used to predict trajectories based on e.g. video data. Studies comparing expert-based and DL-based micro-simulation of shared spaces concerning their accuracy are missing, and so are proven methodologies for combining these approaches into a single agent-based system. In this paper, we propose and compare an expert-based and a DL model and then combine them for trajectory prediction in shared spaces. Simulation results show the combined model to outperform both pure approaches in predicting realistic and collision-free trajectories.
AB - Realistically modelling behaviour and interaction of mixed road users (pedestrians and vehicles) in shared spaces are challenging due to the heterogeneity of transport modes and the absence of classical traffic rules. Existing models have mostly used the expert-based approach, combining symbolic modelling and reasoning paradigm with the hand-crafted encoding of the decision logic. Recently, deep learning (DL) models have been largely used to predict trajectories based on e.g. video data. Studies comparing expert-based and DL-based micro-simulation of shared spaces concerning their accuracy are missing, and so are proven methodologies for combining these approaches into a single agent-based system. In this paper, we propose and compare an expert-based and a DL model and then combine them for trajectory prediction in shared spaces. Simulation results show the combined model to outperform both pure approaches in predicting realistic and collision-free trajectories.
KW - Deep learning
KW - Game theory
KW - Intent detection
KW - Mixed-traffic
UR - http://www.scopus.com/inward/record.url?scp=85095325737&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781450375184
SP - 1878
EP - 1880
BT - Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
A2 - An, Bo
A2 - El Fallah Seghrouchni, Amal
A2 - Sukthankar, Gita
CY - Richland
ER -