Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
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DOI：
10.1109/tits.2021.3074854
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发表时间：
2020-08
影响因子：
8.5
通讯作者：
S. Yu;A. Malawade;Deepan Muthirayan;P. Khargonekar;M. A. Faruque
中科院分区：
文献类型：
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作者：
S. Yu;A. Malawade;Deepan Muthirayan;P. Khargonekar;M. A. Faruque
大量证据表明，评估驾驶决策的主观风险水平可以提高自动驾驶系统（ADS）在典型和复杂驾驶场景中的安全性。在本文中，我们提出了一种新的数据驱动的方法，使用场景图作为中间表示建模的主观风险驾驶演习。我们的方法包括多关系图卷积网络，长短期记忆网络和注意力层。为了训练我们的模型，我们将主观风险评估制定为监督场景分类问题。我们评估我们的模型上的合成变道数据集和实际驾驶数据集与各种驾驶操作。我们表明，我们的方法在大型（96.4% vs. 91.2%）和小型（91.8% vs. 71.2%）车道变换合成数据集上实现了比最先进方法更高的分类准确率，这表明我们的方法即使在小型数据集上也可以有效地学习。我们还表明，我们的模型在车道变换合成数据集上训练，在真实驾驶的车道变换数据集上测试时，平均准确率为87.8%。相比之下，在同一合成数据集上训练的最先进模型在实际驾驶数据集上测试时仅达到70.3%的准确率，这表明我们的方法可以更有效地传递知识。此外，我们证明了空间和时间注意层的加入提高了我们模型的性能和可解释性。最后，我们的研究结果表明，我们的模型可以更准确地评估各种驾驶操作的风险比最先进的模型（86.5%对58.4%，分别）。
There is considerable evidence that evaluating the subjective risk level of driving decisions can improve the safety of Autonomous Driving Systems (ADS) in both typical and complex driving scenarios. 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 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).