# Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction

arXiv: https://arxiv.org/abs/2004.03053 · HTML: https://mlsanity.com/paper/2004.03053/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [Transferable and Adaptable Driving Behavior Prediction](https://mlsanity.com/paper/2202.05140/) (2022)
- [MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs](https://mlsanity.com/paper/2302.00735/) (2023)
- [SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2301.03512/) (2023)
- [Causal-based Time Series Domain Generalization for Vehicle Intention Prediction](https://mlsanity.com/paper/2112.02093/) (2021)
- [Diffusion-Based Environment-Aware Trajectory Prediction](https://mlsanity.com/paper/2403.11643/) (2024)
- [Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction](https://mlsanity.com/paper/2112.06129/) (2021)
- [Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction](https://mlsanity.com/paper/2304.05116/) (2023)
- [Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach](https://mlsanity.com/paper/2403.06086/) (2024)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [Analyzing and Enhancing Closed-loop Stability in Reactive Simulation](https://mlsanity.com/paper/2208.04559/) (2022)
- [Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications](https://mlsanity.com/paper/2504.04419/) (2025)
- [Toward Unified Practices in Trajectory Prediction Research on Bird's-Eye-View Datasets](https://mlsanity.com/paper/2405.00604/) (2024)
- [Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation](https://mlsanity.com/paper/2208.03578/) (2022)
- [Adaptive Decision-Making for Autonomous Vehicles: A Learning-Enhanced Game-Theoretic Approach in Interactive Environments](https://mlsanity.com/paper/2402.11467/) (2024)
- [ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture](https://mlsanity.com/paper/2507.06531/) (2025)
- [Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings](https://mlsanity.com/paper/2606.30777/) (2026)

## Abstract (arXiv)

Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. Several methodologies have been proposed to solve prediction problems under different traffic situations. These works usually combine agent trajectories with either color-coded or vectorized high definition (HD) map as input representations and encode this information for behavior prediction tasks. However, not all the information is relevant in the scene for the forecasting and such irrelevant information may be even distracting to the forecasting in certain situations. Therefore, in this paper, we propose a novel generic representation for various driving environments by taking the advantage of semantics and domain knowledge. Using semantics enables situations to be modeled in a uniform way and applying domain knowledge filters out unrelated elements to target vehicle's future behaviors. We then propose a general semantic behavior prediction framework to effectively utilize these representations by formulating them into spatial-temporal semantic graphs and reasoning internal relations among these graphs. We theoretically and empirically validate the proposed framework under highly interactive and complex scenarios, demonstrating that our method not only achieves state-of-the-art performance, but also processes desirable zero-shot transferability.