关键词: 智慧交通, 轨迹预测, 长短记忆模型, 强化学习

Abstract:

Most of the existing vehicle trajectory predictions are single-target trajectory predictions, without two-way interaction and relational reasoning, and interactive modeling of mixed entities cannot be achieved. To solve the above problems, a fully scalable trajectory prediction model called Q-LSTM is designed by combining the Q-learning algorithm of reinforcement learning and the LSTM network of deep learning. In Q-LSTM model, the LSTM network captures the time characteristics of the vehicle trajectory, and the Q-learning algorithm represents the interaction process of multiple vehicles. Therefore, the Q-LSTM model can realize multi-interaction modeling of a random number of vehicles, and the accuracy of predict the long-term interactive vehicle trajectory is guaranteed. In addition, the relationship between vehicle length and width and coordinates is considered in the model to avoid abnormal collision phenomena, and it is suitable for scenarios of multi-type vehicle trajectory prediction. The performance analysis experiment of the model is carried out on the public data set HighD. The experimenal result proves that the Q-LSTM model has certain advantages in terms of long-term interactive vehicle trajectory prediction accuracy and reduction of collision phenomena.

Key words: intelligent transportation, trajectory prediction, long short term memory, reinforcement learning