Abnormal driving may cause serious danger to both the driver and the public. Existing detectors of abnormal driving behavior are mainly based on shallow models, which require large quantities of labeled data. The acquisition and labelling of abnormal driving data are, however, difficult, labor-intensive and time-consuming. This situation inspires us to rethink the abnormal driving detection problem and to apply deep architecture models. In this study, we establish a novel deep-learning-based model for abnormal driving detection. A stacked sparse autoencoders model is used to learn generic driving behavior features. The model is trained in a greedy layer-wise fashion. As far as the authors know, this is the first time that a deep learning approach is applied using autoencoders as building blocks to represent driving features for abnormal driving detection. In addition, a method for denoising is added to the algorithm to increase the robustness of feature expression. The dropout technology is introduced into the entire training process to avoid overfitting. Experiments carried out on our self-created driving behavior dataset demonstrate that the proposed scheme achieves a superior performance for abnormal driving detection compared to the state-of-the-art.

中文翻译：

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使用标准化驾驶行为数据检测异常驾驶：深度学习方法


不正常驾驶可能对驾驶员和公众造成严重危险。现有的异常驾驶行为检测器主要基于浅层模型，需要大量的标记数据。然而，异常驾驶数据的获取和标记是困难、费力且耗时的。这种情况启发我们重新思考异常驾驶检测问题并应用深层架构模型。在这项研究中，我们建立了一种新颖的基于深度学习的异常驾驶检测模型。堆叠稀疏自动编码器模型用于学习通用驾驶行为特征。该模型以贪婪的分层方式进行训练。据作者所知，这是首次应用深度学习方法，使用自动编码器作为构建块来表示驾驶特征以进行异常驾驶检测。此外，算法中还加入了去噪的方法，以增加特征表达的鲁棒性。整个训练过程引入dropout技术，避免过拟合。在我们自行创建的驾驶行为数据集上进行的实验表明，与最先进的技术相比，所提出的方案在异常驾驶检测方面实现了优越的性能。