College of Agricultural and Life Sciences, University of Wisconsin-Madison, Madison, WI 53705, USA.
State Key Laboratory of Vehicle NVH and Safety Technology, Beijing 100044, China.
Sensors (Basel). 2019 Sep 23;19(19):4108. doi: 10.3390/s19194108.
Real-time capturing of vehicle motion is the foundation of connected vehicles (CV) and safe driving. This study develops a novel vehicle motion detection system (VMDS) that detects lane-change, turning, acceleration, and deceleration using mobile sensors, that is, global positioning system (GPS) and inertial ones in real-time. To capture a large amount of real-time vehicle state data from multiple sensors, we develop a dynamic time warping based algorithm combined with principal component analysis (PCA). Further, the designed algorithm is trained and evaluated on both urban roads and highway using an Android platform. The aim of the algorithm is to alert adjacent drivers, especially distracted drivers, of potential crash risks. Our evaluation results based on driving traces, covering over 4000 miles, conclude that VMDS is able to detect lane-change and turning with an average precision over 76% and speed, acceleration, and brake with an average precision over 91% under the given testing data dataset 1 and 4. Finally, the alerting tests are conducted with a simulator vehicle, estimating the effect of alerting back or front vehicle the surrounding vehicles' motion. Nearly two seconds are gained for drivers to make a safe operation. As is expected, with the help of VMDS, distracted driving decreases and driving safety improves.
实时捕捉车辆运动是车联网（CV）和安全驾驶的基础。本研究开发了一种新型的车辆运动检测系统（VMDS），该系统使用移动传感器（即全球定位系统（GPS）和惯性传感器）实时检测变道、转弯、加速和减速。为了从多个传感器中捕捉大量实时车辆状态数据，我们开发了一种基于动态时间规整的算法，并结合主成分分析（PCA）。进一步地，该设计的算法在城市道路和高速公路上使用 Android 平台进行了训练和评估。该算法的目的是提醒相邻的驾驶员，特别是分神的驾驶员，注意潜在的碰撞风险。我们基于涵盖超过 4000 英里的驾驶轨迹的评估结果表明，在给定的测试数据集 1 和 4 下，VMDS 能够以平均精度超过 76%检测到变道和转弯，以平均精度超过 91%检测到速度、加速和制动。最后，使用模拟器车辆进行了警报测试，估计了对前后车辆的警报如何影响周围车辆的运动。驾驶员有将近两秒钟的时间可以进行安全操作。正如预期的那样，在 VMDS 的帮助下，分神驾驶减少，驾驶安全性提高。
