| 引用本文: | 杜 力,赵 均,杨智宇,谭晓龙,刘国言.横向动力学模型对 UKF 轨迹预测精度的影响分析(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（1）：147-154 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 针对基于数据驱动的轨迹预测研究鲜有使用车辆动力学模型,探讨并解决如何通过动力学模型提高
轨迹预测精度和动力学模型对轨迹预测精度影响作用不清晰等一系列问题。 方法 使用车辆横向动力学模型和二
自由度运动学模型搭建车辆复合模型,实现对 NGSIM 数据集预处理;得到包含车辆动力学模型信息的数据后,通
过 UKF 模型预测车辆轨迹;最后根据实验结果分析车辆横向动力学模型参数对 UKF 模型轨迹预测精度的影响。
结果 所有时段复合模型均能明显提高精度,在 0 ~ 1 s 与运动学模型结合时,β 与 θ 或 γ 同时使用能提高精度;在
1~2 s 和 0~2 s 与运动学模型结合时,γ 会降低精度,β 与 θ 同时使用能提高精度。 结论 与纯运动学模型相比,车
辆横向动力学模型相关参数对 UKF 模型不同时段轨迹预测精度影响情况不同,完整的横向动力学模型与运动学
模型结合在各时段均能明显提高轨迹预测精度,并使精度具备较好的稳定性。 |
| 关键词: 轨迹预测 横向动力学 数据驱动 无迹卡尔曼滤波 |
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| Analysis of the Impact of Lateral Dynamics Model on Trajectory Prediction Accuracy of Unscented Kalman Filter |
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DU Li ZHAO Jun YANG Zhiyu TAN Xiaolong LIU Guoyan
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Key Laboratory of Mechanism Design and Control of Manufacturing Equipment Chongqing Technology and Business
University Chongqing 400067 China
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| Abstract: |
| Objective Since vehicle dynamics models are seldom employed in data-driven trajectory prediction research
this study investigates and resolves a series of critical issues including how such models can improve prediction accuracy
and clarifying the unclear mechanisms through which dynamics models influence trajectory prediction precision. Methods
A composite vehicle model was constructed using the vehicle lateral dynamics model and a two-degree-of-freedom
kinematic model to preprocess the NGSIM dataset. After obtaining data incorporating vehicle dynamics model information
the unscented Kalman filter UKF model was employed to predict vehicle trajectories. Finally the impact of vehicle
lateral dynamics model parameters on the trajectory prediction accuracy of the UKF model was analyzed based on
experimental results. Results The composite model significantly improved accuracy across all time periods. When
combined with the kinematic model during the 0~1 s interval simultaneously using β and either θ or γ increased
accuracy. During the 1~2 s and 0~2 s intervals using γ decreased accuracy while simultaneously using β and θ
improved accuracy. Conclusion Compared with the pure kinematic model the relevant parameters of the vehicle lateral
dynamics model exhibit different impacts on the trajectory prediction accuracy of the UKF model at different time periods.Integrating the complete lateral dynamics model with the kinematic model significantly enhances trajectory prediction
accuracy across all time periods and provides better stability in accuracy. |
| Key words: trajectory prediction lateral dynamics data-driven unscented Kalman filter |
