Research Group of Operating Systems and Distributed Systems, University of Siegen, Hölderlinstr. 3, 57076 Siegen, Germany.
Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.
Sensors (Basel). 2022 May 10;22(10):3634. doi: 10.3390/s22103634.
Identifying accident patterns is one of the most vital research foci of driving analysis. Environmental or safety applications and the growing area of fleet management all benefit from accident detection contributions by minimizing the risk vehicles and drivers are subject to, improving their service and reducing overhead costs. Some solutions have been proposed in the past literature for automated accident detection that are mainly based on traffic data or external sensors. However, traffic data can be difficult to access, while external sensors can end up being difficult to set up and unreliable, depending on how they are used. Additionally, the scarcity of accident detection data has limited the type of approaches used in the past, leaving in particular, machine learning (ML) relatively unexplored. Thus, in this paper, we propose a ML framework for automated car accident detection based on mutimodal in-car sensors. Our work is a unique and innovative study on detecting real-world driving accidents by applying state-of-the-art feature extraction methods using basic sensors in cars. In total, five different feature extraction approaches, including techniques based on feature engineering and feature learning with deep learning are evaluated on the strategic highway research program (SHRP2) naturalistic driving study (NDS) crash data set. The main observations of this study are as follows: (1) CNN features with a SVM classifier obtain very promising results, outperforming all other tested approaches. (2) Feature engineering and feature learning approaches were finding different best performing features. Therefore, our fusion experiment indicates that these two feature sets can be efficiently combined. (3) Unsupervised feature extraction remarkably achieves a notable performance score.
识别事故模式是驾驶分析中最重要的研究焦点之一。环境或安全应用以及车队管理领域的不断发展都受益于事故检测的贡献，通过最大限度地降低车辆和驾驶员面临的风险，提高他们的服务质量并降低运营成本。过去的文献中已经提出了一些用于自动化事故检测的解决方案，这些解决方案主要基于交通数据或外部传感器。然而，交通数据可能难以获取，而外部传感器可能由于使用方式的不同而难以设置和不可靠。此外，事故检测数据的稀缺性限制了过去使用的方法类型，特别是机器学习 (ML) 方法相对较少被探索。因此，在本文中，我们提出了一种基于车内多模态传感器的自动化汽车事故检测的 ML 框架。我们的工作是一项独特而创新的研究，通过在汽车中使用基本传感器应用最先进的特征提取方法来检测现实世界中的驾驶事故。总共评估了五种不同的特征提取方法，包括基于特征工程和深度学习的特征学习技术，应用于战略公路研究计划 (SHRP2) 自然驾驶研究 (NDS) 事故数据集。本研究的主要观察结果如下：(1) 基于 SVM 分类器的 CNN 特征获得了非常有前景的结果，优于所有其他测试方法。(2) 特征工程和特征学习方法找到了不同的最佳表现特征。因此，我们的融合实验表明，这两个特征集可以有效地结合。(3) 无监督特征提取显著达到了显著的性能得分。
