【摘要】 为提升行车安全，实现自动驾驶车辆正确的决策规划，提出基于扩展卡尔曼滤波(EKF)-门控循环单元(GRU)的车辆轨迹预测方法，结合学习方法与物理模型，在提升预测精度的同时，提高轨迹预测的合理性。首先，基于GRU构建预测网络，通过提取车辆的历史轨迹特征预测车辆的纵向加速度及横摆角速度；其次，基于车辆非线性运动学构建EKF状态估计器，结合观测值生成车辆未来有限时域的行驶轨迹；最后，在高速公路多车轨迹数据集NGSIM I-80和US-101上进行轨迹预测方法验证。结果表明：采用传统的物理模型生成预测轨迹，其最终距离误差(FDE)、均方根误差(RMSE)、平均距离误差(ADE)值分别为6.48、7.69和3.03 m。相比之下，利用EKF-GRU生成的预测轨迹表现出更高的准确性，对应的数值分别为5.45、6.67和2.56 m,分别提升15.90%、13.26%和15.51%。更多还原

【Abstract】 To enhance the driving safety and achieve correct decision planning for autonomous vehicles, a safe driving trajectory prediction method based on EKF-GRU was proposed. By combining learning-based methods with physics-based approaches, the prediction accuracy was improved and the rationality of the predicted trajectories was enhanced. In the first step of this method, a prediction network was constructed based on GRU to predict the longitudinal acceleration and yaw angular velocity of vehicles by extracting historical trajectory features. In the second step, an EKF state estimator was built based on the nonlinear vehicle kinematics to generate the vehicle’s future limited-time trajectory, incorporating the observations obtained previously. The trajectory prediction method was validated on the NGSIM I-80 and US-101 multi-vehicle trajectory datasets. Experimental results demonstrate that the final distance errors(FDE), root mean square errors(RMSE), and average distance errors(ADE) of the predicted trajectories generated by traditional physics-based methods are 6.48, 7.69 and 3.03 meters, respectively. In contrast, trajectories predicted using EKF-GRU exhibit higher accuracy, and the corresponding values are 5.45, 6.67 and 2.56 meters, respectively. This represents improvements of 15.90%, 13.26% and 15.51%.更多还原