论文标题
使用CNN-LSTM网络对连接车辆的互动感知轨迹预测
Interaction-Aware Trajectory Prediction of Connected Vehicles using CNN-LSTM Networks
论文作者
论文摘要
预测在拥挤交通中周围车辆的未来轨迹是自动驾驶汽车的基本能力之一。在充血中，车辆的未来运动是与周围车辆相互作用的结果。拥塞的车辆可能在相对较短的距离内有许多邻居，而只有一小部分邻居主要影响其未来轨迹。在这项工作中，提出了一种相互作用的方法，该方法可以预测自我车辆与八个周围车辆的相互作用的未来轨迹。车辆的动力学由具有共享权重的LSTM编码，并用简单的CNN提取相互作用。对所提出的模型进行了对从公共访问的NGSIM US-101数据集提取的轨迹进行训练和测试。定量实验结果表明，根据根平方误差（RMSE），所提出的模型优于先前的模型。结果可视化表明，该模型能够预测车辆变化引起的未来轨迹在车辆运行明显的横向运动以启动车道变化。
Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the basic abilities of an autonomous vehicle. In congestion, a vehicle's future movement is the result of its interaction with surrounding vehicles. A vehicle in congestion may have many neighbors in a relatively short distance, while only a small part of neighbors affect its future trajectory mostly. In this work, An interaction-aware method which predicts the future trajectory of an ego vehicle considering its interaction with eight surrounding vehicles is proposed. The dynamics of vehicles are encoded by LSTMs with shared weights, and the interaction is extracted with a simple CNN. The proposed model is trained and tested on trajectories extracted from the publicly accessible NGSIM US-101 dataset. Quantitative experimental results show that the proposed model outperforms previous models in terms of root-mean-square error (RMSE). Results visualization shows that the model is able to predict future trajectory induced by lane change before the vehicle operate obvious lateral movement to initiate lane changing.
