基于手机传感器轨迹的路面地物检测方法

Road Surface Object Detection from Mobile Phone Based Sensor Trajectories

针对传统路面地物信息采集方法存在的数据采集周期长、成本高等问题,提出了一种基于手机传感器轨迹的城市路面地物检测方法。利用手机记录车辆行驶过程中各传感器数据的变化,分析经过姿态校正后的加速度数据,研究加速度变化与路况之间的联系,构建BP神经网络模型,并使用已采集数据对模型进行训练,以识别路面地物。实验结果表明,基于手机传感器轨迹的路面地物检测方法具有快速准确地检测路面地物信息的能力,且地物检测准确率大于85%,能够较为准确地检测路面地物,文中基于手机姿态传感器对手机加速度传感器姿态进行了实时矫正,利用手机垂直于路面的加速度变化检测路面地物,因此所提方法具有手机加速度传感器姿态无关性,此外,所提方法对硬件设备要求低、数据采集效率高,降低了路面地物信息采集的成本,具有广泛的应用前景。

Aimed at the problem of low efficiency and high cost in the traditional road surface object collection procedure,the method of road surface object recognition from mobile phone based sensor trajectories is proposed.Mobile phones are used to record the data changes of various sensors in the process of driving,and then the acceleration data after attitude correction are analyzed to find the relationship between the acceleration trend and the road condition.Finally,the constructe the BP neural network model,and use the acquired data to train the BP neural network model to recognize the road surface object and its position.Experiment results show that,the road surface object can be fast and accurately recognized by the mobile phone based senor trajectories,and the accuracy can be higher than 85%.In this paper,attitude of mobile acceleration sensor has carried on the real time correction.Because the acceleration changing of the Mobile phones is perpendicular to the road,we use the acceleration change to detecte the road feature.The method has nothing to do with a mobile phone accelerometer gesture,in addition,hardware requirements of the method are low,the efficiency of data acquisition is high,which reduce the cost of the road surface features information acquisition.