论文标题
具有行为数据和LSTM Resnet分类器的智能汽车中的身份识别
Identity Recognition in Intelligent Cars with Behavioral Data and LSTM-ResNet Classifier
论文作者
论文摘要
如今，汽车舱中的身份识别是一项至关重要的任务，提供了一个很大的应用领域，从个性化智能汽车到适合驾驶员的身体和行为需求到增加安全性和保障。但是，已发布方法的性能和适用性仍然不适合在系列汽车中使用，需要改进。在本文中，我们研究了具有时间序列分类（TSC）和深神经网络的汽车舱中的人类身份识别。我们使用气体和制动踏板压力作为模型的输入。在日常情况下，在驾驶过程中，这些数据很容易收集。由于我们的分类器几乎没有内存要求，并且不需要任何输入数据进行预审查，因此我们只能在一个Intel I5-3210M处理器上训练。我们的分类方法基于LSTM和Resnet的组合。该网络在一部分的裸露培训中均优于重新NET和LSTM模型，分别训练了35.9％和53.85％的精度。我们在10驱动器子集的nudrive子集中达到79.49％的最终精度，在UTDRIVE的5驱动器子集上达到了96.90％。
Identity recognition in a car cabin is a critical task nowadays and offers a great field of applications ranging from personalizing intelligent cars to suit drivers physical and behavioral needs to increasing safety and security. However, the performance and applicability of published approaches are still not suitable for use in series cars and need to be improved. In this paper, we investigate Human Identity Recognition in a car cabin with Time Series Classification (TSC) and deep neural networks. We use gas and brake pedal pressure as input to our models. This data is easily collectable during driving in everyday situations. Since our classifiers have very little memory requirements and do not require any input data preproccesing, we were able to train on one Intel i5-3210M processor only. Our classification approach is based on a combination of LSTM and ResNet. The network trained on a subset of NUDrive outperforms the ResNet and LSTM models trained solely by 35.9 % and 53.85 % accuracy respectively. We reach a final accuracy of 79.49 % on a 10-drivers subset of NUDrive and 96.90 % on a 5-drivers subset of UTDrive.
