论文标题
通过多个传感器数据融合的车辆路线预测
Vehicle Route Prediction through Multiple Sensors Data Fusion
论文作者
论文摘要
车辆路线预测是车辆活动性的重要任务之一。这是减少事故并增加人类生活中舒适性的手段之一。随着某些机器学习和深度学习库的发展，路由预测的任务变得更加简单。同时，安全性和隐私问题始终存在于车辆通信以及路线预测中。因此，我们提出了一个框架，该框架将减少车辆通信中的这些问题，并预测在十字路口中车辆的路线。具体而言，我们提出的框架由两个模块组成，两者都按顺序工作。我们框架的第一个模块使用深度学习来识别车辆车牌号。然后，第二个模块使用机器学习的监督学习算法来通过使用速度差和先前的移动性模式作为机器学习算法的特征来预测车辆的路线。实验结果表明我们框架的准确性。
Vehicle route prediction is one of the significant tasks in vehicles mobility. It is one of the means to reduce the accidents and increase comfort in human life. The task of route prediction becomes simpler with the development of certain machine learning and deep learning libraries. Meanwhile, the security and privacy issues are always lying in the vehicle communication as well as in route prediction. Therefore, we proposed a framework which will reduce these issues in vehicle communication and predict the route of vehicles in crossroads. Specifically, our proposed framework consists of two modules and both are working in sequence. The first module of our framework using a deep learning for recognizing the vehicle license plate number. Then, the second module using supervised learning algorithm of machine learning for predicting the route of the vehicle by using velocity difference and previous mobility patterns as the features of machine learning algorithm. Experiment results shows that accuracy of our framework.
