论文标题
自动驾驶汽车时间序列预测的不同方法的比较
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles
论文作者
论文摘要
作为各种技术的结合，自动驾驶汽车可以自己完成一系列驾驶任务，例如感知，决策，计划和控制。由于没有人类驾驶员来处理紧急情况，因此未来的运输信息对于自动车辆很重要。本文提出了不同的方法，以预测最近的邻域（NN），模糊编码（FC）和长期记忆（LSTM）的自动驾驶汽车的时间序列。首先，引入了这三种方法的配方和操作过程。然后，将车辆速度视为案例研究，并利用现实世界中的数据集通过这些技术来预测未来的信息。最后，分析和讨论了提出方法的性能，优点和缺点。
As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and control. Since there is no human driver to handle the emergency situation, future transportation information is significant for automated vehicles. This paper proposes different methods to forecast the time series for autonomous vehicles, which are the nearest neighborhood (NN), fuzzy coding (FC), and long short term memory (LSTM). First, the formulation and operational process for these three approaches are introduced. Then, the vehicle velocity is regarded as a case study and the real-world dataset is utilized to predict future information via these techniques. Finally, the performance, merits, and drawbacks of the presented methods are analyzed and discussed.
