An Ensemble Learning-Based Vehicle Steering Detector Using Smartphones
An Ensemble Learning-Based Vehicle Steering Detector Using Smartphones
复制标题
使用智能手机的基于集成学习的车辆转向检测器
DOI：
10.1109/tits.2019.2909107
复制
发表时间：
2020-05-01
影响因子：
8.5
通讯作者：
Liu, Xue
中科院分区：
文献类型：
--
作者：
Ouyang, Zhenchao;Niu, Jianwei;Liu, Xue
由于智能手机易于使用，近年来人们对使用手机作为车辆转向检测的传感和计算平台越来越感兴趣。然而，与车载诊断（OBD）系统相比，智能手机传感器的精度相对较低，这往往导致精度降低。本文提出了一种结合启发式算法的基于集成学习的智能手机车辆转向检测模型。集成学习以其强大的泛化能力、较高的准确率和快速的收敛性得到了广泛的认可。然而，将集成学习方法应用于基于智能手机的车辆转向检测，由于智能手机存储的限制、功耗的限制以及对实时性的要求，面临着许多挑战。为了应对这些挑战，我们提出了一系列技术来降低模型的复杂性和能耗，同时保持较高的检测精度。通过实际数据集验证了该系统的性能，准确率达到97.37%。我们还用不同的智能手机对北京的真实道路环境进行了两个案例研究。
Due to easy access to smartphones, recent years have witnessed an increasing interest in using the mobile phone as a sensing and computation platform for vehicle steering detection. However, relatively lower accuracy of smartphone sensors than on-board diagnostic (OBD)-based systems often leads to lower accuracy. We propose an ensemble learning-based model combined with the heuristic algorithm for smartphone-based vehicle steering detection in this paper. Ensemble learning has been widely recognized for its powerful generalization capability, high accuracy, and rapid convergence. However, applying the ensemble learning approach to steering detection of the smartphone-based vehicle entails many challenges due to the limitation of smartphone storage, the constraint on power consumption, and the requirement of being real-time. To address these challenges, we propose a series of techniques to reduce the complexity of the model and energy consumption, while at the same time maintaining high detection accuracy. The performance of the proposed system has been demonstrated using a real dataset and can achieve an accuracy of 97.37%. We also conduct two case studies on real road environment in Beijing with different smartphones.