Exploring Dynamic Context for Multi-path Trajectory Prediction
This addresses the problem of safe autonomous system deployment by improving multi-path trajectory prediction in dynamic traffic environments, representing a strong incremental advance with novel method integration.
The paper tackles the challenge of predicting multiple possible future trajectories for agents in traffic scenarios by proposing DCENet, a framework that encodes dynamic spatial-temporal context using self-attention and CVAE, achieving state-of-the-art performance on the Trajnet benchmark and superior results on the inD benchmark.
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.