Exploring Dynamic Context for Multi-path Trajectory Prediction

• URL: http://arxiv.org/abs/2010.16267v3
• Date: Wed, 24 Mar 2021 10:28:47 GMT
• Title: Exploring Dynamic Context for Multi-path Trajectory Prediction
• Authors: Hao Cheng, Wentong Liao, Xuejiao Tang, Michael Ying Yang, Monika Sester, Bodo Rosenhahn
• Abstract summary: We propose a novel framework, named Dynamic Context Network (DCENet) In our framework, the spatial context between agents is explored by using self-attention architectures. A set of future trajectories for each agent is predicted conditioned on the learned spatial-temporal context.
• Score: 33.66335553588001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, the two-stream encoders are trained to learn temporal context between steps by taking the respective observed trajectories and the extracted dynamic spatial context as input. 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 one of the most popular challenging benchmarks for trajectory forecasting 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 is conducted to validate the effectiveness of each proposed module. Our code is available at https://github.com/wtliao/DCENet.

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