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