不确定性车辆路口的轨迹预测

在城市道路中,实时、准确、可靠地对移动车辆进行轨迹预测具有极高的应用价值,不仅可以提供准确的基于位置的服务,而且可以帮助过往车辆预知前方的交通状况。目前,移动车辆的轨迹预测方法主要基于历史轨迹的欧氏空间进行,并未考虑在受限路网中采用不确定性历史数据的车辆轨迹预测。针对这一问题,提出一种补全路径的基于马尔科夫链的轨迹预测方法,其优势在于:重新定义了补全路径算法,弥补了不确定性历史数据的不完整性,利用马尔科夫链低时间复杂度、高预测准确度的优势实现预测,避免了因频繁模式挖掘带来的查询时间过长而影响预测效率以及存在多余噪声影响轨迹预测准确率的问题。通过真实数据和实验分析表明:在参数设置相同的情况下,该方法比挖掘频繁轨迹模式算法的预测准确率平均提高了18.8%,预测时间平均缩减了80.4%。因此,该方法对于车辆路口的轨迹预测具有较高的预测准确率,并且能预测一系列的车辆未来轨迹。

Uncertain Vehicle Intersection Trajectory Prediction

In the city road,real-time,accurate and reliable trajectory prediction of mobile vehicles can bring very high application value,which can not only provide accurate location-based services,but also can help the vehicle to predict the traffic situation.At present,the trajectory prediction method of moving vehicles is mainly based on the precise historical trajectory in Euclidean space,and does not consider the vehicle trajectory prediction with uncertain historical data in restricted road network.A trajectory prediction method based on Markov chain was proposed to solve this problem.Its advantages include redefining the path algorithm of completion,making up for the incompleteness of uncertain historical data,and achieving prediction by using the characteristics of low time complexity and high prediction accuracy with Markov chain.This method avoids the problem of low prediction accuracy caused by too much query time due to the frequent pattern mining and the excess noise.The results show that under the same parameter setting,the prediction accuracy of the method is 18.8% higher than that of the mining frequent trajectory model,and the prediction time is reduced by 80.4% on average.Therefore,the method has high prediction accuracy for the trajectory prediction of the vehicle intersection,and achieves the prediction of a series of vehicle future trajectories.