TY - GEN

T1 - Exploring Dynamic Context for Multi-path Trajectory Prediction

AU - Cheng, Hao

AU - Liao, Wentong

AU - Tang, Xuejiao

AU - Yang, Michael Ying

AU - Sester, Monika

AU - Rosenhahn, Bodo

N1 - Publisher Copyright: © 2021 IEEE

PY - 2021

Y1 - 2021

N2 - To accurately predict future positions of different agents in traffic scenarios is crucial for safely deploying intelligent autonomous systems in the real-world environment. However, it remains a challenge due to the behavior of a target agent being affected by other agents dynamically, and there being more than one socially possible paths the agent could take. In this paper, we propose a novel framework, named Dynamic Context Encoder Network (DCENet). In our framework, first, the spatial context between agents is explored by using self-attention architectures. Then, two LSTM encoders are trained to learn temporal context between steps by taking the observed trajectories and the extracted dynamic spatial context as input, respectively. The spatial-temporal context is encoded into a latent space using a Conditional Variational Auto-Encoder (CVAE) module. Finally, a set of future trajectories for each agent is predicted conditioned on the learned spatial-temporal context by sampling from the latent space, repeatedly. DCENet is evaluated on the largest and most challenging trajectory forecasting benchmark Trajnet and reports a new state-of-the-art performance. It also demonstrates superior performance evaluated on the benchmark InD for mixed traffic at intersections. A series of ablation studies are conducted to validate the effectiveness of each proposed module. Our code is available at https://github.com/wtliao/DCENet.

AB - To accurately predict future positions of different agents in traffic scenarios is crucial for safely deploying intelligent autonomous systems in the real-world environment. However, it remains a challenge due to the behavior of a target agent being affected by other agents dynamically, and there being more than one socially possible paths the agent could take. In this paper, we propose a novel framework, named Dynamic Context Encoder Network (DCENet). In our framework, first, the spatial context between agents is explored by using self-attention architectures. Then, two LSTM encoders are trained to learn temporal context between steps by taking the observed trajectories and the extracted dynamic spatial context as input, respectively. The spatial-temporal context is encoded into a latent space using a Conditional Variational Auto-Encoder (CVAE) module. Finally, a set of future trajectories for each agent is predicted conditioned on the learned spatial-temporal context by sampling from the latent space, repeatedly. DCENet is evaluated on the largest and most challenging trajectory forecasting benchmark Trajnet and reports a new state-of-the-art performance. It also demonstrates superior performance evaluated on the benchmark InD for mixed traffic at intersections. A series of ablation studies are conducted to validate the effectiveness of each proposed module. Our code is available at https://github.com/wtliao/DCENet.

KW - cs.CV

KW - cs.MA.

UR - http://www.scopus.com/inward/record.url?scp=85124508826&partnerID=8YFLogxK

U2 - 10.1109/ICRA48506.2021.9562034

DO - 10.1109/ICRA48506.2021.9562034

M3 - Conference contribution

SN - 978-1-7281-9078-5

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 12795

EP - 12801

BT - Proceedings - IEEE International Conference on Robotics and Automation (ICRA)

PB - Institute of Electrical and Electronics Engineers Inc.

ER -