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Real-Time Vehicle Motion Detection and Motion Altering for Connected Vehicle: Algorithm Design and Practical Applications

机译：互联车辆的实时车辆运动检测与运动改变：算法设计与实际应用

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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.
机译：实时捕获车辆运动是互联车辆（CV）和安全驾驶的基础。这项研究开发了一种新颖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车道变化，转弯，加速和减速。为了从多个传感器捕获大量实时车辆状态数据，我们开发了一种基于动态时间扭曲的算法，并结合了主成分分析（PCA）。此外，使用Android平台在城市道路和高速公路上对设计的算法进行训练和评估。该算法的目的是提醒相邻的驾驶员，尤其是分心的驾驶员潜在的碰撞风险。我们基于行驶里程超过4000英里的评估结果得出的结论是，在给定的测试下，VMDS能够检测平均准确度超过76％的车道变更和转弯以及平均准确度超过91％的速度，加速度和刹车数据集1和4。最后，用模拟车辆进行警报测试，以估计向后或向前方车辆发出警报的效果。驾驶员获得了将近两秒钟的安全操作时间。不出所料，在VMDS的帮助下，分心驾驶减少了，驾驶安全性得到了改善。

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