多交互车辆轨迹预测研究

现有的车辆轨迹预测大多是单目标轨迹预测,无双向交互和关系推理,不能实现混合实体的交互建模.针对上述问题,结合强化学习的Q-learning算法和深度学习的LSTM网络,设计一个完全可扩展的轨迹预测模型Q-LSTM.该模型中,LSTM网络捕获了车辆轨迹的时间特性,而Q-learning算法则表示了多车辆的交互过程,因此Q...

Research on Multi-interaction Vehicle Trajectory Prediction

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.