# Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions

arXiv: https://arxiv.org/abs/2009.06435 · HTML: https://mlsanity.com/paper/2009.06435/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

## Built on by (most cited first, 30 of 34)

- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [PyG 2.0: Scalable Learning on Real World Graphs](https://mlsanity.com/paper/2507.16991/) (2025)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction](https://mlsanity.com/paper/2111.06123/) (2021)
- [A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics](https://mlsanity.com/paper/2207.12599/) (2022)
- [RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding](https://mlsanity.com/paper/2304.08600/) (2023)
- [roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs](https://mlsanity.com/paper/2109.01183/) (2021)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding](https://mlsanity.com/paper/2407.05910/) (2024)
- [DROID: Driver-centric Risk Object Identification](https://mlsanity.com/paper/2106.13201/) (2021)
- [Explainable Action Prediction through Self-Supervision on Scene Graphs](https://mlsanity.com/paper/2302.03477/) (2023)
- [MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment](https://mlsanity.com/paper/2307.08065/) (2023)
- [World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving](https://mlsanity.com/paper/2507.12762/) (2025)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems](https://mlsanity.com/paper/2404.05605/) (2024)
- [Exposing Privacy Risks in Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.17222/) (2025)
- [RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments](https://mlsanity.com/paper/2207.12321/) (2022)
- [Vehicle-group-based Crash Risk Prediction and Interpretation on Highways](https://mlsanity.com/paper/2402.12415/) (2024)
- [GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units](https://mlsanity.com/paper/2502.06921/) (2025)
- [PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network](https://mlsanity.com/paper/2404.10263/) (2024)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [VAGNet: Vision-based Accident Anticipation with Global Features](https://mlsanity.com/paper/2604.09305/) (2026)
- [Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs](https://mlsanity.com/paper/2308.13658/) (2023)
- [STDG: Semi-Teacher-Student Training Paradigram for Depth-guided One-stage Scene Graph Generation](https://mlsanity.com/paper/2309.08179/) (2023)
- [DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding](https://mlsanity.com/paper/2311.06497/) (2023)
- [On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance](https://mlsanity.com/paper/2411.17152/) (2024)
- [Predicting Driver's Perceived Risk: a Model Based on Semi-Supervised Learning Strategy](https://mlsanity.com/paper/2504.12665/) (2025)
- [MsFIN: Multi-scale Feature Interaction Network for Traffic Accident Anticipation](https://mlsanity.com/paper/2509.19227/) (2025)
- [What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Planners](https://mlsanity.com/paper/2510.00619/) (2025)

## Abstract (arXiv)

Despite impressive advancements in Autonomous Driving Systems (ADS), navigation in complex road conditions remains a challenging problem. There is considerable evidence that evaluating the subjective risk level of various decisions can improve ADS' safety in both normal and complex driving scenarios. However, existing deep learning-based methods often fail to model the relationships between traffic participants and can suffer when faced with complex real-world scenarios. Besides, these methods lack transferability and explainability. To address these limitations, we propose a novel data-driven approach that uses scene-graphs as intermediate representations. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers for modeling the subjective risk of driving maneuvers. To train our model, we formulate this task as a supervised scene classification problem. We consider a typical use case to demonstrate our model's capabilities: lane changes. We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) synthesized datasets, also illustrating that our approach can learn effectively even from smaller datasets. We also show that our model trained on a synthesized dataset achieves an average accuracy of 87.8% when tested on a real-world dataset compared to the 70.3% accuracy achieved by the state-of-the-art model trained on the same synthesized dataset, showing that our approach can more effectively transfer knowledge. Finally, we demonstrate that the use of spatial and temporal attention layers improves our model's performance by 2.7% and 0.7% respectively, and increases its explainability.