# Exploring Dynamic Context for Multi-path Trajectory Prediction

arXiv: https://arxiv.org/abs/2010.16267 · HTML: https://mlsanity.com/paper/2010.16267/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model](https://mlsanity.com/paper/2209.07857/) (2022)
- [SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs](https://mlsanity.com/paper/2102.06361/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information](https://mlsanity.com/paper/2106.00559/) (2021)
- [TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models](https://mlsanity.com/paper/2308.05985/) (2023)
- [The Stanford Drone Dataset is More Complex than We Think: An Analysis of Key Characteristics](https://mlsanity.com/paper/2203.11743/) (2022)
- [A Fast and Map-Free Model for Trajectory Prediction in Traffics](https://mlsanity.com/paper/2307.09831/) (2023)
- [An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images](https://mlsanity.com/paper/2308.05026/) (2023)
- [Toward Unified Practices in Trajectory Prediction Research on Bird's-Eye-View Datasets](https://mlsanity.com/paper/2405.00604/) (2024)
- [Remember and Recall: Associative-Memory-based Trajectory Prediction](https://mlsanity.com/paper/2410.02201/) (2024)
- [To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software](https://mlsanity.com/paper/2508.20892/) (2025)
- [MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles](https://mlsanity.com/paper/2407.05811/) (2024)
- [ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning](https://mlsanity.com/paper/2606.16558/) (2026)

## Abstract (arXiv)

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.