# SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network

arXiv: https://arxiv.org/abs/2002.12609 · HTML: https://mlsanity.com/paper/2002.12609/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models](https://mlsanity.com/paper/2403.18344/) (2024)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs](https://mlsanity.com/paper/2302.00735/) (2023)
- [Diffusion-Based Environment-Aware Trajectory Prediction](https://mlsanity.com/paper/2403.11643/) (2024)
- [Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems](https://mlsanity.com/paper/2210.08057/) (2022)
- [Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles](https://mlsanity.com/paper/2206.08809/) (2022)
- [Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction](https://mlsanity.com/paper/2304.05116/) (2023)
- [SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction](https://mlsanity.com/paper/2405.03809/) (2024)
- [Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting](https://mlsanity.com/paper/2111.13324/) (2021)
- [Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric](https://mlsanity.com/paper/2302.01259/) (2023)
- [Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles](https://mlsanity.com/paper/2303.04340/) (2023)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services](https://mlsanity.com/paper/2307.01684/) (2023)
- [Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics](https://mlsanity.com/paper/2408.12609/) (2024)
- [Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends](https://mlsanity.com/paper/2401.00713/) (2024)
- [Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction](https://mlsanity.com/paper/2111.13350/) (2021)
- [Fighter flight trajectory prediction based on spatio-temporal graphcial attention network](https://mlsanity.com/paper/2405.08034/) (2024)
- [LoRFT: Benchmarking Long-Range Vehicle Trajectory Reconstruction from Fixed Highway Cameras](https://mlsanity.com/paper/2607.19911/) (2026)

## Abstract (arXiv)

Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. Since there is no pre-defined number of interacting vehicles participate in, the prediction network has to be scalable with respect to the vehicle number in order to guarantee the consistency in terms of both accuracy and computational load. In this paper, the first fully scalable trajectory prediction network, SCALE-Net, is proposed that can ensure both higher prediction performance and consistent computational load regardless of the number of surrounding vehicles. The SCALE-Net employs the Edge-enhance Graph Convolutional Neural Network (EGCN) for the inter-vehicular interaction embedding network. Since the proposed EGCN is inherently scalable with respect to the graph node (an agent in this study), the model can be operated independently from the total number of vehicles considered. We evaluated the scalability of the SCALE-Net on the publically available NGSIM datasets by comparing variations on computation time and prediction accuracy per single driving scene with respect to the varying vehicle number. The experimental test shows that both computation time and prediction performance of the SCALE-Net consistently outperform those of previous models regardless of the level of traffic complexities.