Lu Dang-Nhac, Nguyen Duc-Nhan, Nguyen Thi-Hau, Nguyen Ha-Nam
University of Engineering and Technology, Vietnam National University in Hanoi (VNU-UET), Hanoi 123105, Vietnam.
Academy of Journalism and Communication, Hanoi 123105, Vietnam.
Sensors (Basel). 2018 Mar 29;18(4):1036. doi: 10.3390/s18041036.
In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers' vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.
在本文中，我们提出了一种灵活的组合系统，即车辆模式 - 驾驶活动检测系统（VADS），它能够检测旅行者当前的车辆模式或驾驶活动。我们提出的系统设计为计算轻量级，并且对旅行者车辆模式或驾驶事件的变化响应非常快。车辆模式检测模块负责识别机动车，如汽车、公交车和摩托车，以及非机动车，例如步行和骑自行车。它仅依赖加速度计数据以尽量减少智能手机的能耗。相比之下，驾驶活动检测模块使用从智能手机的加速度计、陀螺仪和磁力计收集的数据来检测各种驾驶活动，即停车、直行、左转和右转。此外，我们提出了一种方法，用于从训练数据集中计算每种车辆模式和每个驾驶事件的优化数据窗口大小和优化重叠率。实验结果表明，该策略显著提高了整体预测准确率。此外，还进行了大量实验来比较不同特征集（时域特征、频域特征、 Hjorth特征）的影响以及各种分类算法（随机森林、朴素贝叶斯、决策树J48、K近邻、支持向量机）对预测准确率的影响。当使用随机森林分类器和包含时域特征、频域特征和Hjorth特征的特征集时，我们的系统在检测车辆模式方面的平均准确率为98.33%，在识别摩托车手的驾驶事件方面的平均准确率为98.95%。此外，在台湾新北市HTC公司的一个公共数据集上，我们的框架获得了97.33%的整体准确率，这大大高于现有技术水平。
