SceneGraph-Risk-Assessment dataset

This dataset is released with our research paper titled “Scene-graph Augmented Data-driven Risk Assessment of Autonomous Vehicle Decisions” (https://arxiv.org/abs/2009.06435). In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers. To train our model, we formulate the problem of subjective risk assessment as a supervised scene classification problem. We evaluate our model on both synthetic lane-changing datasets and real-driving datasets with various driving maneuvers. 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%) lane-changing synthesized datasets, illustrating that our approach can learn effectively even from small datasets. We also show that our model trained on a lane-changing synthesized dataset achieves an average accuracy of 87.8\% when tested on a real-driving lane-changing dataset. In comparison, the state-of-the-art model trained on the same synthesized dataset only achieved 70.3% accuracy when tested on the real-driving dataset, showing that our approach can transfer knowledge more effectively. Moreover, we demonstrate that the addition of spatial and temporal attention layers improves our model’s performance and explainability. Finally, our results illustrate that our model can assess the risk of various driving maneuvers more accurately than the state-of-the-art model (86.5% vs. 58.4%, respectively). In this dataset, we release both synthetic and real-driving scene-graph risk assessment datasets (e.g. syn-271-sg, syn-1043-sg, honda-571-sg, honda-1361-sg) and synthetic video clip datasets (syn-271-image, syn-1043-image) for helping our users reproduce the results presented in our paper. We also hope that this dataset can be potentially useful in their research. Please kindly consider citing us if you find this dataset useful in your research. @article{yu2020scene, title={Scene-graph augmented data-driven risk assessment of autonomous vehicle decisions}, author={Yu, Shih-Yuan and Malawade, Arnav V and Muthirayan, Deepan and Khargonekar, Pramod P and Al Faruque, Mohammad A}, journal={arXiv preprint arXiv:2009.06435}, year={2020}}

本数据集随我们的研究论文《基于场景图增强的数据驱动自动驾驶车辆决策风险评估》（https://arxiv.org/abs/2009.06435）一同发布。在论文中，我们提出了一种新颖的数据驱动方法，该方法利用场景图作为中间表示来建模驾驶操作的直观风险。我们的方法包括多关系图卷积网络、长短期记忆网络以及注意力层。为了训练我们的模型，我们将主观风险评估问题表述为一个监督场景分类问题。我们将在合成车道变换数据集和真实驾驶数据集上评估我们的模型，这些数据集包含了多种驾驶操作。我们的研究表明，我们的方法在大型（96.4% 对比 91.2%）和小型（91.8% 对比 71.2%）车道变换合成数据集上均实现了比现有技术更高的分类准确率，这表明我们的方法即便从小数据集中也能有效地学习。此外，我们在真实驾驶车道变换数据集上测试时发现，基于车道变换合成数据集训练的我们的模型平均准确率为 87.8%，而相同合成数据集上训练的现有技术模型在真实驾驶数据集上的准确率仅为 70.3%，这表明我们的方法能够更有效地迁移知识。此外，我们还证明了添加空间和时间注意力层可以提升我们模型的表现力和可解释性。最后，我们的结果展示了我们的模型在评估各种驾驶操作的风险方面比现有技术模型更为精确（分别达到 86.5% 对比 58.4%）。在本数据集中，我们发布了合成和真实驾驶场景图风险评估数据集（例如，syn-271-sg、syn-1043-sg、honda-571-sg、honda-1361-sg）以及合成视频片段数据集（syn-271-image、syn-1043-image），以帮助用户重现论文中展示的结果。我们也希望这一数据集可能对他们的研究有所帮助。如若发现本数据集对您的科研工作有益，恳请予以引用。@article{yu2020scene, title={Scene-graph augmented data-driven risk assessment of autonomous vehicle decisions}, author={Yu, Shih-Yuan and Malawade, Arnav V and Muthirayan, Deepan and Khargonekar, Pramod P and Al Faruque, Mohammad A}, journal={arXiv preprint arXiv:2009.06435}, year={2020}}