| 张俊豪,陶贤露,潘树国,高旺,薛佳慧.基于虚拟障碍物决策的自适应两阶段轨迹规划方法[J].电子测量与仪器学报,2025,39(9):75-86 |
| 基于虚拟障碍物决策的自适应两阶段轨迹规划方法 |
| Adaptive two-stage trajectory planning method based onvirtual obstacle decision-making |
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| DOI： |
| 中文关键词: 轨迹规划 自适应聚集采样策略 动态规划 二次规划 随机森林 Carla仿真 |
| 英文关键词:trajectory planning adaptive aggregation sampling strategy dynamic programming quadratic programming random forest Carla simulation |
| 基金项目:江苏省前沿技术研发计划项目（BF2024009）资助 |
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| Author | Institution |
| Zhang Junhao | State Key Laboratory of comprehensive PNT Network and Equipment Technology, School of Instrument
Science and Engineering, Southeast University,Nanjing 210096, China |
| Tao Xianlu | State Key Laboratory of comprehensive PNT Network and Equipment Technology, School of Instrument
Science and Engineering, Southeast University,Nanjing 210096, China |
| Pan Shuguo | State Key Laboratory of comprehensive PNT Network and Equipment Technology, School of Instrument
Science and Engineering, Southeast University,Nanjing 210096, China |
| Gao Wang | State Key Laboratory of comprehensive PNT Network and Equipment Technology, School of Instrument
Science and Engineering, Southeast University,Nanjing 210096, China |
| Xue Jiahui | State Key Laboratory of comprehensive PNT Network and Equipment Technology, School of Instrument
Science and Engineering, Southeast University,Nanjing 210096, China |
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| 中文摘要: |
| 针对传统轨迹规划算法在狭长空间规划成功率低、自适应程度差等问题,提出一种基于虚拟障碍物决策的自适应两阶段轨迹规划方法。首先，通过动态规划和二次规划完成无人车辆的路径规划和速度规划；其次，提出了一种自适应聚集采样策略解决了狭长空间较难通过的问题；最后，构建了一种基于随机森林的虚拟障碍物决策模型提升了无人车辆在不同会车情况决策的合理性。在Carla（car learning to act）仿真平台的结果表明，相比于传统方法，该方法在狭窄区域的静态多障碍物避障时路径长度、路径曲率分别降低了2.4%、85.6%，规划成功率、安全性及其稳定性分别提高了约20%、20.6%和44.9%；在狭窄区域的动态多障碍物避障时路径长度、路径曲率分别降低了8.3%、76.4%，规划成功率、安全性及其稳定性分别提高了约36%、78.2%和45.3%。最后，将方法部署到实际无人车辆中，在狭长的走廊场景设置障碍物进行测试，验证了方法的有效性。 |
| 英文摘要: |
| To address the limitations of traditional trajectory planning algorithms in confined spaces, such as low planning success rates, poor adaptability, and deviations from human driving habits, this paper proposes an adaptive two-stage trajectory planning algorithm integrated with virtual obstacle decision-making. The first stage combines dynamic programming and quadratic programming to achieve path planning and velocity optimization for autonomous vehicles. Subsequently, an adaptive aggregation sampling strategy is introduced to resolve navigation challenges in narrow environments. Finally, a random forest-based virtual obstacle decision model is developed to enhance decision-making rationality under diverse vehicle interaction scenarios. The results on the simulation platform Carla show that, compared with the traditional method, the path length and path curvature of the proposed method are reduced by 2.4% and 85.6% respectively, and the success rate of planning, safety and stability are improved by about 20%, 20.6% and 44.9% respectively in the static multi-obstacle avoidance in narrow areas. In the dynamic multi-obstacle avoidance in narrow areas, the path length and path curvature are reduced by 8.3% and 76.4%, respectively, and the planning success rate, safety and stability are improved by about 36%, 78.2% and 45.3%, respectively. Finally, the method was deployed to the actual unmanned vehicle, and the obstacles were set up in the narrow and long corridor scene for testing, which verified the effectiveness of the method. |
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