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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%.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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