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Multitask Learning Assisted Driver Identity Authentication and Driving Behavior Evaluation

机译：多任务学习辅助驱动程序身份认证和驾驶行为评估

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摘要

The industrial Internet of Things has become the new driving force for the automobile industry, making people's travel increasingly convenient. However, there are still a multitude of challenges that need to be tackled, including but not limited to illegal driver detection, legal driver identification, and driving behavior evaluation. At present, many researchers have attempted to solve issues of illegal driver detection and legal driver identification by using deep learning network, but there are still quite a few limitations in the collection and analysis of driving behavior data. Moreover, the problem of driving behavior evaluation has been paid little attention. Therefore, in this article we conduct a comprehensive study on driving behavior habits and establish a multitask learning (MTL) network to solve the abovementioned problems. First, we collect original data from a real vehicle and extract the driving behavior characteristics. Then, a novel MTL network composed of long short-term memory network, support vector domain description model and feedforward neural network is established, which achieves illegal driver detection, legal driver identification, and driving behavior evaluation. Extensive experiments illustrate that the proposed MTL network not only supports parallel learning to reduce time and space costs, but also has excellent performances and robustness for the three tasks.
机译：工业互联网已经成为汽车工业的新动力，使人们的旅行日益方便。然而，仍然需要解决多种挑战，包括但不限于非法驾驶员检测，法律驱动程序识别和驾驶行为评估。目前，许多研究人员试图通过使用深度学习网络解决非法驾驶员检测和法律驱动程序识别的问题，但驾驶行为数据的收集和分析仍有相当多的限制。此外，驾驶行为评估的问题几乎没有注意。因此，在本文中，我们对驾驶行为习惯进行了全面的研究，并建立了一个多任务学习（MTL）网络来解决上述问题。首先，我们从真正的车辆收集原始数据并提取驾驶行为特征。然后，建立了一种新的MTL网络，由长短短期存储器网络组成，支持向量域描述模型和前馈神经网络，这实现了非法驾驶员检测，法律驱动程序识别和驾驶行为评估。广泛的实验说明，所提出的MTL网络不仅支持并行学习，以降低时间和空间成本，而且还具有以下三个任务的优异性能和鲁棒性。

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