# Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving

arXiv: https://arxiv.org/abs/2109.12764 · HTML: https://mlsanity.com/paper/2109.12764/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (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)
- [Multi-Agent Tensor Fusion for Contextual Trajectory Prediction](https://mlsanity.com/paper/1904.04776/) (2019)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs](https://mlsanity.com/paper/1805.05499/) (2018)
- [GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/1907.07792/) (2019)
- [How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction](https://mlsanity.com/paper/1801.06523/) (2018)
- [Bike Flow Prediction with Multi-Graph Convolutional Networks](https://mlsanity.com/paper/1807.10934/) (2018)
- [Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction](https://mlsanity.com/paper/1905.06335/) (2019)
- [Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks](https://mlsanity.com/paper/1906.02815/) (2019)
- [Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network](https://mlsanity.com/paper/1909.05665/) (2019)
- [Interaction-aware Kalman Neural Networks for Trajectory Prediction](https://mlsanity.com/paper/1902.10928/) (2019)

## Built on by (most cited first, 30 of 36)

- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation](https://mlsanity.com/paper/2204.09429/) (2022)
- [Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning](https://mlsanity.com/paper/2211.00848/) (2022)
- [Holistic Graph-based Motion Prediction](https://mlsanity.com/paper/2301.13545/) (2023)
- [EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Prediction](https://mlsanity.com/paper/2303.17027/) (2023)
- [Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks](https://mlsanity.com/paper/2309.01981/) (2023)
- [Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems](https://mlsanity.com/paper/2210.08057/) (2022)
- [Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios](https://mlsanity.com/paper/2306.05478/) (2023)
- [Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning](https://mlsanity.com/paper/2409.11676/) (2024)
- [Interaction-Aware Trajectory Planning for Autonomous Vehicles with Analytic Integration of Neural Networks into Model Predictive Control](https://mlsanity.com/paper/2301.05393/) (2023)
- [Distilling Knowledge for Short-to-Long Term Trajectory Prediction](https://mlsanity.com/paper/2305.08553/) (2023)
- [A Novel Temporal Multi-Gate Mixture-of-Experts Approach for Vehicle Trajectory and Driving Intention Prediction](https://mlsanity.com/paper/2308.00533/) (2023)
- [SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction](https://mlsanity.com/paper/2405.03809/) (2024)
- [Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2411.06087/) (2024)
- [Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation](https://mlsanity.com/paper/2404.01746/) (2024)
- [MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2604.10169/) (2026)
- [A Cooperation-Aware Lane Change Method for Autonomous Vehicles](https://mlsanity.com/paper/2201.10746/) (2022)
- [ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in Emergency Scenarios](https://mlsanity.com/paper/2405.04100/) (2024)
- [FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction](https://mlsanity.com/paper/2411.16747/) (2024)
- [Global Update Guided Federated Learning](https://mlsanity.com/paper/2204.03920/) (2022)
- [Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction](https://mlsanity.com/paper/2406.11941/) (2024)
- [STF: Spatial Temporal Fusion for Trajectory Prediction](https://mlsanity.com/paper/2311.18149/) (2023)
- [Spatial-Temporal Conditional Random Field for Human Trajectory Prediction](https://mlsanity.com/paper/2311.18198/) (2023)
- [Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity with Interpretability](https://mlsanity.com/paper/2312.12123/) (2023)
- [Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends](https://mlsanity.com/paper/2401.00713/) (2024)
- [Trajectory Prediction for Autonomous Driving using Agent-Interaction Graph Embedding](https://mlsanity.com/paper/2410.23298/) (2024)
- [Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction](https://mlsanity.com/paper/2508.17797/) (2025)
- [Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles](https://mlsanity.com/paper/2304.13732/) (2023)

## Abstract (arXiv)

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles the spatial interactions using a graph convolutional network (GCN), and captures the temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our proposed scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM).Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance.