计算机与现代化 ›› 2022, Vol. 0 ›› Issue (05): 114-118.

• 人工智能 • 上一篇    下一篇

面向消除通信时延的车辆轨迹多步预测方法

  

  1. (长安大学信息工程学院,陕西西安710064)
  • 出版日期:2022-06-08 发布日期:2022-06-08
  • 作者简介:齐战硕(1996—),男,江苏徐州人,硕士研究生,研究方向:混合现实与智能辅助驾驶,E-mail: 245063446@qq.com; 高彦东(1995—),男,硕士研究生,研究方向:虚拟现实,自动驾驶测试,E-mail: ydgao.chd@qq.com。
  • 基金资助:
    国家重点研发计划项目(2018YFB1600800)

Multi-step Prediction Method of Vehicle Trajectory for Eliminating Communication Delay

  1. (School of Information Engineering, Chang’an Univeristy, Xi’an 710064, China)
  • Online:2022-06-08 Published:2022-06-08

摘要: 现阶段的车路协同测试环境下通常采用具有实时性特征的系统,为了解决车路协同测试的实时系统中容易出现通信时延问题,本文提出一种面向消除通信时延的车辆轨迹多步预测方法,通过构建LSTM神经网络模型,将高频采样序列进行拆分和重组后对其建立新的序列,并按照不同间隔的差分序列逐条输入,经过对各序列下轨迹点的单点预测,形成未来一段距离的车辆行驶轨迹,进而实现车辆轨迹的多步预测。实验结果表明,本文提出的多步轨迹预测方法能够消除93.94%的通信和系统时延,并且多步轨迹预测相比于单步轨迹预测在中远距离下的MSE增长率减少了7.47个百分点,具有很好的时延消除特性和误差控制能力。

关键词: 通信时延, 长短期记忆网络, 轨迹预测, 车路协同测试

Abstract: In the current vehicle-road collaborative test environment, systems with real-time characteristics are usually used. In order to solve the communication delay problem that easily occurs in the real-time system of vehicle-road collaborative test, this paper proposes a multi-step predicion method of vehicle trajectory oriented to eliminate communication delay. Through the construction of LSTM neural network model, the high-frequency sampling sequence is split and reorganized to establish a new sequence, and the difference sequence of different intervals is input one by one. After single-point prediction of trajectory points under each sequence, the vehicle trajectory for a certain distance in the future is formed, and then the multi-step prediction of the vehicle trajectory is realized. The experimental results show that the multi-step trajectory forecasting method proposed in this paper can eliminate 93.94% of the communication and system delays, and the multi-step trajectory prediction reduces the MSE growth rate by 7.47 percentage points at medium and long distances compared to the single-step trajectory prediction, which has good time delay elimination characteristic and error control ability.

Key words: communication delay, long and short-term memory network, trajectory prediction, vehicle-route collaborative test