考虑场景时空特征的多目标最优类人驾驶轨迹规划

吕彦直; 林如宁; 林本祥; 魏超; 李锦涛; 马婧

doi:10.12382/bgxb.2025.0259

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考虑场景时空特征的多目标最优类人驾驶轨迹规划

更新时间：2026-04-24

- 考虑场景时空特征的多目标最优类人驾驶轨迹规划
- Multi-objective Optimal Human-like Driving Trajectory Planning Considering Scenarios Spatiotemporal Characteristics
- 兵工学报 2026年47卷第1期页码：250259
- 作者机构：
  
  中兵智能创新研究院有限公司，北京 100072
  群体协同与自主实验室，北京 100072
  北京理工大学机械与车辆学院，北京 100081
- 作者简介：
  
  *通信作者邮箱：lvyanzhi_123@163.com
- 基金信息：
- DOI：10.12382/bgxb.2025.0259
  中图分类号： U461
- 收稿：2025-04-08，
  
  网络首发：2026-02-11，
  
  纸质出版：2026-01-31
- 稿件说明：
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吕彦直, 林如宁, 林本祥, 等. 考虑场景时空特征的多目标最优类人驾驶轨迹规划[J]. 兵工学报, 2026,47(1):250259.

LÜ Yanzhi, LIN Runing, LIN Benxiang, et al. Multi-objective Optimal Human-like Driving Trajectory Planning Considering Scenarios Spatiotemporal Characteristics[J]. Acta Armamentarii, 2026, 47(1): 250259.
吕彦直, 林如宁, 林本祥, 等. 考虑场景时空特征的多目标最优类人驾驶轨迹规划[J]. 兵工学报, 2026,47(1):250259. DOI： 10.12382/bgxb.2025.0259.

LÜ Yanzhi, LIN Runing, LIN Benxiang, et al. Multi-objective Optimal Human-like Driving Trajectory Planning Considering Scenarios Spatiotemporal Characteristics[J]. Acta Armamentarii, 2026, 47(1): 250259. DOI： 10.12382/bgxb.2025.0259.

摘要

为充分利用驾驶场景时空特征、使轨迹规划满足多种驾驶需求、得到安全稳定的类人驾驶规划轨迹，构建考虑场景时空特征的多目标最优类人驾驶轨迹规划模型。基于图论的方法，研究动态驾驶场景全局时空交互特征融合方法，根据模仿学习的理论，构建考虑时空特征和他车预测轨迹影响的类人驾驶轨迹规划网络模型，针对网络输出的多个候选规划轨迹，利用多目标优选的思想对其进行综合评价并择优，进而得到动态场景下的多目标最优类人驾驶规划轨迹，通过设计轨迹规划仿真实验，对提出的轨迹规划方法进行测试验证。实验结果表明，规划模型可高效处理驾驶场景时空交互特征，对其他交通参与者不同的驾驶行为做出准确判断和实时反应，输出多目标最优类人驾驶规划轨迹。

Abstract

In order to make full use of spatiotemporal characteristics of driving scenarios

make trajectory planning meet various driving needs

and obtain safe and stable human-like driving trajectories

a multi-objective optimal human-like driving trajectory planning model considering the spatiotemporal characteristics of scenarios is constructed. Based on the graph theory method

the global spatiotemporal interaction feature fusion method of dynamic driving scenarios is studied. According to the theory of imitation learning

a human-like driving trajectory planning network model considering the spatiotemporal characteristics and the influence of other vehicles' predicted trajectories is constructed. For those multiple candidate planning trajectories output by the network

the idea of multi-objective optimization is used to comprehensively evaluate and select them

and then the multi-objective optimal human-like driving trajectory in dynamic scenarios is obtained. The proposed trajectory planning method is tested and verified by simulation experiments. The experimental results show that the planning model can efficiently handle the spatiotemporal interaction characteristics of driving scenarios

make accurate judgments and real-time responses to different driving behaviors of other traffic participants

and output multi-objective optimal human-like driving planning trajectories.

关键词

Keywords

references

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