Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seitenumfang | 9 |
| ISBN (elektronisch) | 978-1-7281-4149-7 |
| ISBN (Print) | 978-1-7281-4150-3 |
| Publikationsstatus | Veröffentlicht - 2020 |
Abstract
Trajectory prediction in urban mixed-traffic zones (a.k. a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
- Informatik (insg.)
- Artificial intelligence
- Entscheidungswissenschaften (insg.)
- Entscheidungswissenschaften (sonstige)
- Sozialwissenschaften (insg.)
- Ausbildung bzw. Denomination
- Mathematik (insg.)
- Modellierung und Simulation
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2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9294296.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - MCENET
T2 - Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic
AU - Cheng, Hao
AU - Liao, Wentong
AU - Yang, Michael Ying
AU - Sester, Monika
AU - Rosenhahn, Bodo
N1 - Funding Information: This work is supported by the German Research Founda- tion (DFG) through the Research Training Group SocialCars (GRK 1931) and grants COVMAP (RO 2497/12-2).
PY - 2020
Y1 - 2020
N2 - Trajectory prediction in urban mixed-traffic zones (a.k. a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies.
AB - Trajectory prediction in urban mixed-traffic zones (a.k. a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies.
KW - cs.CV
KW - cs.CY
KW - cs.MA
UR - http://www.scopus.com/inward/record.url?scp=85095370359&partnerID=8YFLogxK
U2 - 10.1109/ITSC45102.2020.9294296
DO - 10.1109/ITSC45102.2020.9294296
M3 - Conference contribution
SN - 978-1-7281-4150-3
BT - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
ER -