# Trajectory Prediction with Graph-based Dual-scale Context Fusion

arXiv: https://arxiv.org/abs/2111.01592 · HTML: https://mlsanity.com/paper/2111.01592/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 44 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021)

## Built on by (most cited first, 19 of 19)

- [Traj-MAE: Masked Autoencoders for Trajectory Prediction](https://mlsanity.com/paper/2303.06697/) (2023)
- [MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction](https://mlsanity.com/paper/2308.10280/) (2023)
- [SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving](https://mlsanity.com/paper/2402.02519/) (2024)
- [What Truly Matters in Trajectory Prediction for Autonomous Driving?](https://mlsanity.com/paper/2306.15136/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Motion Forecasting in Continuous Driving](https://mlsanity.com/paper/2410.06007/) (2024)
- [BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction](https://mlsanity.com/paper/2306.14161/) (2023)
- [GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction](https://mlsanity.com/paper/2506.21121/) (2025)
- [Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics](https://mlsanity.com/paper/2507.12083/) (2025)
- [Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric](https://mlsanity.com/paper/2302.01259/) (2023)
- [Future-Aware Interaction Network For Motion Forecasting](https://mlsanity.com/paper/2503.06565/) (2025)
- [Trajectory Forecasting on Temporal Graphs](https://mlsanity.com/paper/2207.00255/) (2022)
- [FFINet: Future Feedback Interaction Network for Motion Forecasting](https://mlsanity.com/paper/2311.04512/) (2023)
- [ProIn: Learning to Predict Trajectory Based on Progressive Interactions for Autonomous Driving](https://mlsanity.com/paper/2403.16374/) (2024)
- [A Fast and Map-Free Model for Trajectory Prediction in Traffics](https://mlsanity.com/paper/2307.09831/) (2023)
- [Improving Motion Forecasting for Autonomous Driving with the Cycle Consistency Loss](https://mlsanity.com/paper/2211.00149/) (2022)
- [LANet: A Lane Boundaries-Aware Approach For Robust Trajectory Prediction](https://mlsanity.com/paper/2507.01308/) (2025)
- [ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture](https://mlsanity.com/paper/2507.06531/) (2025)
- [TaPD: Temporal-adaptive Progressive Distillation for Observation-Adaptive Trajectory Forecasting in Autonomous Driving](https://mlsanity.com/paper/2603.06231/) (2026)

## Abstract (arXiv)

Motion prediction for traffic participants is essential for a safe and robust automated driving system, especially in cluttered urban environments. However, it is highly challenging due to the complex road topology as well as the uncertain intentions of the other agents. In this paper, we present a graph-based trajectory prediction network named the Dual Scale Predictor (DSP), which encodes both the static and dynamical driving context in a hierarchical manner. Different from methods based on a rasterized map or sparse lane graph, we consider the driving context as a graph with two layers, focusing on both geometrical and topological features. Graph neural networks (GNNs) are applied to extract features with different levels of granularity, and features are subsequently aggregated with attention-based inter-layer networks, realizing better local-global feature fusion. Following the recent goal-driven trajectory prediction pipeline, goal candidates with high likelihood for the target agent are extracted, and predicted trajectories are generated conditioned on these goals. Thanks to the proposed dual-scale context fusion network, our DSP is able to generate accurate and human-like multi-modal trajectories. We evaluate the proposed method on the large-scale Argoverse motion forecasting benchmark, and it achieves promising results, outperforming the recent state-of-the-art methods.