Intelligent transportation brings huge benefits to humans’ life and Industrial production in terms of vehicle control and traffic management. Now, the development of edge-cloud computing has once again promoted intelligent transportation into a new era. However, the development of intelligent transportation inevitably produces a large amount of data, which brings new challenges to data privacy protection and security. In this paper, we propose to develop an improved trajectory prediction framework based on the self-adaptive trajectory prediction model (SATP), which could significantly enhance traffic safety in transportation systems. The proposed framework is capable of guaranteeing the accurate trajectory prediction of moving target under different application scenarios. In particular, to reduce the size of original trajectory point data collected by sensors, the angle change and minimum description length (MDL) principle are first combined to remove the redundant points in raw trajectories. The obtained points can then be reduced for model using the two-step clustering method. To further enhance the prediction performance, we add the “self-transfer” to the original model to solve the problems that the state of original SATP model may be discontinuous. Furthermore, we propose to develop a trajectory complementation method based on Bezier curve to improve the prediction accuracy. Finally, by comparing the two-step clustering method with the commonly-used SinglePass and density-based clustering method (DBCM) algorithms, the proposed two-step clustering policy greatly reduce the time cost of clustering. At the same time, by comparing the improved SATP model with the original model, the results show that the improved SATP method can greatly improve the speed of prediction model.

中文翻译：

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自适应轨迹预测可改善基于云边缘的交通系统中的交通安全

智能交通在车辆控制和交通管理方面为人类的生活和工业生产带来了巨大的好处。现在，边缘云计算的发展再次将智能交通推向了一个新时代。但是，智能交通的发展不可避免地会产生大量数据，这给数据隐私保护和安全性带来了新的挑战。本文提出了一种基于自适应轨迹预测模型（SATP）的改进的轨迹预测框架，该框架可以显着提高交通系统的交通安全性。所提出的框架能够保证在不同应用场景下运动目标的准确轨迹预测。特别是，为了减小传感器收集的原始轨迹点数据的大小，首先将角度变化和最小描述长度（MDL）原理结合起来，以去除原始轨迹中的多余点。然后可以使用两步聚类方法为模型减少获得的点。为了进一步提高预测性能，我们在原始模型中添加了“自转移”以解决原始SATP模型的状态可能不连续的问题。此外，我们提出了一种基于贝塞尔曲线的轨迹互补方法，以提高预测精度。最后，通过将两步聚类方法与常用的SinglePass和基于密度的聚类方法（DBCM）算法进行比较，提出的两步聚类策略大大降低了聚类的时间成本。与此同时，