Details
| Original language | English |
|---|---|
| Pages (from-to) | 163-175 |
| Number of pages | 13 |
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
| Volume | 205 |
| Early online date | 12 Oct 2023 |
| Publication status | Published - Nov 2023 |
Abstract
Trajectory prediction has been a long-standing problem in intelligent systems like autonomous driving and robot navigation. Models trained on large-scale benchmarks have made significant progress in improving prediction accuracy. However, the importance on efficiency for real-time applications has been less emphasized. This paper proposes an attention-based graph model, named GATraj, which achieves a good balance of prediction accuracy and inference speed. We use attention mechanisms to model the spatial–temporal dynamics of agents, such as pedestrians or vehicles, and a graph convolutional network to model their interactions. Additionally, a Laplacian mixture decoder is implemented to mitigate mode collapse and generate diverse multimodal predictions for each agent. GATraj achieves state-of-the-art prediction performance at a much higher speed when tested on the ETH/UCY datasets for pedestrian trajectories, and good performance at about 100 Hz inference speed when tested on the nuScenes dataset for autonomous driving. We conduct extensive experiments to analyze the probability estimation of the Laplacian mixture decoder and compare it with a Gaussian mixture decoder for predicting different multimodalities. Furthermore, comprehensive ablation studies demonstrate the effectiveness of each proposed module in GATraj.
Keywords
- Autonomous driving, Graph model, Mixture density network, Pedestrian, Trajectory prediction
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Engineering (miscellaneous)
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 205, 11.2023, p. 163-175.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - GATraj
T2 - A graph- and attention-based multi-agent trajectory prediction model
AU - Cheng, Hao
AU - Liu, Mengmeng
AU - Chen, Lin
AU - Broszio, Hellward
AU - Sester, Monika
AU - Yang, Michael Ying
N1 - Funding Information: This work is partially supported by the MSCA European Postdoctoral Fellowships under the 101062870 – VeVuSafety project and partially performed in the framework of project KaBa (Kamerabasierte Bewegungsanalyse aller Verkehrsteilnehmer für automatisiertes Fahren) supported by the European Regional Development Fund at VISCODA company.
PY - 2023/11
Y1 - 2023/11
N2 - Trajectory prediction has been a long-standing problem in intelligent systems like autonomous driving and robot navigation. Models trained on large-scale benchmarks have made significant progress in improving prediction accuracy. However, the importance on efficiency for real-time applications has been less emphasized. This paper proposes an attention-based graph model, named GATraj, which achieves a good balance of prediction accuracy and inference speed. We use attention mechanisms to model the spatial–temporal dynamics of agents, such as pedestrians or vehicles, and a graph convolutional network to model their interactions. Additionally, a Laplacian mixture decoder is implemented to mitigate mode collapse and generate diverse multimodal predictions for each agent. GATraj achieves state-of-the-art prediction performance at a much higher speed when tested on the ETH/UCY datasets for pedestrian trajectories, and good performance at about 100 Hz inference speed when tested on the nuScenes dataset for autonomous driving. We conduct extensive experiments to analyze the probability estimation of the Laplacian mixture decoder and compare it with a Gaussian mixture decoder for predicting different multimodalities. Furthermore, comprehensive ablation studies demonstrate the effectiveness of each proposed module in GATraj.
AB - Trajectory prediction has been a long-standing problem in intelligent systems like autonomous driving and robot navigation. Models trained on large-scale benchmarks have made significant progress in improving prediction accuracy. However, the importance on efficiency for real-time applications has been less emphasized. This paper proposes an attention-based graph model, named GATraj, which achieves a good balance of prediction accuracy and inference speed. We use attention mechanisms to model the spatial–temporal dynamics of agents, such as pedestrians or vehicles, and a graph convolutional network to model their interactions. Additionally, a Laplacian mixture decoder is implemented to mitigate mode collapse and generate diverse multimodal predictions for each agent. GATraj achieves state-of-the-art prediction performance at a much higher speed when tested on the ETH/UCY datasets for pedestrian trajectories, and good performance at about 100 Hz inference speed when tested on the nuScenes dataset for autonomous driving. We conduct extensive experiments to analyze the probability estimation of the Laplacian mixture decoder and compare it with a Gaussian mixture decoder for predicting different multimodalities. Furthermore, comprehensive ablation studies demonstrate the effectiveness of each proposed module in GATraj.
KW - Autonomous driving
KW - Graph model
KW - Mixture density network
KW - Pedestrian
KW - Trajectory prediction
UR - http://www.scopus.com/inward/record.url?scp=85174064136&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2209.07857
DO - 10.48550/arXiv.2209.07857
M3 - Article
VL - 205
SP - 163
EP - 175
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -