Smartphone localization inside a moving car for prevention of distracted driving
Smartphone localization inside a moving car for prevention of distracted driving
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DOI：
10.1080/00423114.2019.1578889
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发表时间：
2020-02-01
影响因子：
3.6
通讯作者：
Rajamani, Rajesh
中科院分区：
文献类型：
--
作者：
Johnson, Gregory;Rajamani, Rajesh
本文讨论了一种新颖的算法，以自动识别智能手机在移动车辆中的位置，以便检测驾驶员是在驾驶员使用还是仅仅是汽车的乘客。该检测有用于预防分心驾驶的应用程序，可用于自动禁用手机功能，例如当手机位于驾驶员座椅上时发短信。智能手机本地化问题中的挑战源于仅在典型手机上完全使用已经可用的加速度计和陀螺仪，并且需要在汽车驾驶员或乘客携带的同时允许手机的任何未知的3维方向。首先，通过在电话参考框架中识别车辆的纵向和垂直轴来确定手机的实时方向。这提供了旋转矩阵，用于在手机上测量的加速度和角速度的转换，以围绕汽车轴的加速度和角速度。接下来，在减速过程中汽车的前到背面的俯仰动力学以及转弯期间的左右滚动动力学的特征是检测手机是否在驾驶员座位上。使用传感器信号和机器学习算法的交叉旋转形式对滚动和音高动力学的表征进行了形式化。模拟和广泛的实验都用于证明开发的系统可以准确确定驾驶员是否携带手机。开发的技术对于iPhone和其他智能手机可能非常有用，这些手机目前只能检测到手机是否在移动汽车上，但无法检测到驾驶员还是乘客使用它是在使用它。
This paper discusses a novel algorithm to automatically identify the position of a smartphone inside a moving vehicle, so as to detect whether it is being used by the driver or just a passenger of the car. This detection has applications to the prevention of distracted driving and can be used to automatically disable phone features such as texting when the phone is located in the driver's seat. The challenges in the smartphone localisation problem come from the need to entirely use only accelerometers and gyroscopes already available on a typical phone, and the need to allow for any unknown 3-dimensional orientation of the phone while being carried by the driver or passenger of the car. First, the phone's real-time orientation is determined by identifying the vehicle's longitudinal and vertical axes in the phone reference frame. This provides the rotational matrix for conversion of accelerations and angular velocities measured on the phone to accelerations and angular velocities about the car axes. Next, the front-to-back pitching dynamics of the car during deceleration and the side-to-side roll dynamics during turning are characterised to detect whether the phone is in the driver's seat. The characterisation of the roll and pitch dynamics are formalised using cross-covariances of sensor signals and a machine learning algorithm. Both simulations and extensive experiments are used to show that the developed system can accurately determine if the phone is being carried by the driver. The developed technology can be extremely useful for iPhones and other smartphones which can currently only detect whether the phone is on a moving car, but cannot detect whether it is being used by a driver or a passenger.