尽管自动驾驶系统（ADS）取得了令人印象深刻的进步，但复杂路况下的导航仍然是一个具有挑战性的问题。有相当多的证据表明，评估各种决策的主观风险水平可以提高ADS在正常和复杂驾驶场景中的安全性。然而，现有的基于深度学习的方法通常无法对交通参与者之间的关系进行建模，并且在面对复杂的现实世界场景时可能会受到影响。此外，这些方法缺乏可移植性和可解释性。为了解决这些限制，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。我们的方法包括一个多关系图卷积网络，一个长短期记忆网络，和注意力层建模的主观风险驾驶演习。为了训练我们的模型，我们制定了这个任务作为一个监督场景分类问题。我们考虑一个典型的用例来展示我们模型的功能：车道变化。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%）合成数据集上都比最先进的方法实现了更高的分类准确率，这也说明我们的方法即使从较小的数据集也可以有效地学习。我们还表明，我们在合成数据集上训练的模型在真实世界数据集上测试时，平均准确率为87.8%，而在相同合成数据集上训练的最先进模型的准确率为70.3%，这表明我们的方法可以更有效地传递知识。最后，我们证明了空间和时间注意层的使用分别将我们模型的性能提高了2.7%和0.7%，并提高了其可解释性。
英文摘要：
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.
Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems
School of Energy and Electrical Engineering, Chang’an University, Xi’an, China（能源与电气工程学院，长安大学，西安，中国）；School of Information Engineering, Chang’an University, Xi’an, China（信息工程学院，长安大学，西安，中国）
Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles
SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris（SAMOVAR， Télécom SudParis， Institut Polytechnique de Paris）；Universidad Carlos III de Madrid
Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making
Machine Intelligence, Interaction, and Imagination (Mi3) Laboratory（机器智能、交互与想象实验室）；Laboratory for Intelligent and Safe Automobiles (LISA)（智能与安全汽车实验室）；Johns Hopkins University（约翰霍普金斯大学）；Center for Medicinal Cannabis Research (CMCR)（医药大麻研究中心）
