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Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks

机译：基于LSTM递归神经网络的基于传感器数据融合的驾驶行为分类

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摘要

We are presenting a novel approach for the task of driving behavior classification based on stacked LSTM Recurrent Neural Networks. Given a nine different sensor data captured using a smart phone internal sensors during a naturalistic driving sessions, we formulated the driving behavior classification problem as time-series classification task. Whereas, given a window sequence of fused feature vectors of sensor data at any time step of a driving trip, we can accurately classify the driving behavior during that window sequence from a three distinctive driving behavior classes, namely normal, aggressive or drowsy driving. We evaluated our proposed Stacked-LSTM model on one of the recent naturalistic driving behavior analysis and classification dataset, UAH-DriveSet. Our proposed Stacked-LSTM model has achieved state-of-the-art results on the UAH-DriveSet with much higher true positive rate as well as lower false positive rate in comparison to the baseline approach. We have also compared the performance of our proposed Stacked-LSTM model against a number of the common classification algorithms used in the driving behavior classification and analysis studies and we achieved F1-measure score of 91% with an improvement of more than 10% over the closest compared approaches.
机译：我们正在提出一种基于堆叠LSTM递归神经网络的驾驶行为分类任务的新方法。假设在自然驾驶过程中使用智能手机内部传感器捕获了九种不同的传感器数据，我们将驾驶行为分类问题表述为时间序列分类任务。鉴于在行驶行程的任何时间步上给出了传感器数据融合特征向量的窗口序列，我们可以从三个独特的驾驶行为类别（即正常驾驶，积极驾驶或困倦驾驶）中准确地对该窗口序列中的驾驶行为进行分类。我们在最近的自然驾驶行为分析和分类数据集UAH-DriveSet之一上评估了我们提出的Stacked-LSTM模型。与基线方法相比，我们提出的Stacked-LSTM模型在UAH-DriveSet上获得了最先进的结果，其真阳性率更高，而假阳性率更低。我们还将提议的Stacked-LSTM模型的性能与驾驶行为分类和分析研究中使用的多种常用分类算法进行了比较，我们的F1测量得分达到91％，并且提高了10％以上最接近的比较方法。

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