吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (6): 1570-1581.doi: 10.13229/j.cnki.jdxbgxb.20231280

• 交通运输工程·土木工程 • 上一篇    

考虑舒适度的智能汽车人工蜂群轨迹规划方法

谢宪毅1,2(),张明君3(),金立生1,周彬4,胡涛1,白宇飞1   

  1. 1.燕山大学 车辆与能源学院，河北 秦皇岛 066004
    2.清华大学 汽车安全与节能国家重点实验室，北京 100084
    3.中国汽车技术研究中心有限公司 中汽研汽车检验中心（天津）有限公司，天津 300300
    4.北京航空航天大学 车路一体智能交通全国重点实验室，北京 102206
  • 收稿日期:2023-11-20 出版日期:2024-06-01 发布日期:2024-07-23
  • 通讯作者: 张明君 E-mail:xiexianyi@ysu.edu.cn;zhangmingjun@catarc.ac.cn
  • 作者简介:谢宪毅（1989-），男，讲师，博士.研究方向：智能车辆决策规划与控制、智能车辆人机共驾. E-mail：xiexianyi@ysu.edu.cn
  • 基金资助:
    国家自然科学基金项目(52072333);清华大学汽车安全与节能国家重点实验室开放基金项目(KFY2211);河北省省级科技计划项目(F2021203107);国家重点研发计划项目(2022YFF0604901)

Artificial bee colony trajectory planning algorithm for intelligent vehicles considering comfortable

Xian-yi XIE1,2(),Ming-jun ZHANG3(),Li-sheng JIN1,Bin ZHOU4,Tao HU1,Yu-fei BAI1   

  1. 1.School of Vehicles and Energy，Yanshan University，Qinhuangdao 066004，China
    2.State Key Laboratory of Automotive Safety and Energy，Tsinghua University，Beijing 100084，China
    3.CATARC Automotive Inspection Center （Tianjin） Co. ，Ltd. ，China Automotive Technology and Research Center Co. ，Ltd. ，Tianjin 300300，China
    4.State Key Laboratory of Vehicle Road Integrated Intelligent Transportation System，Beihang University，Beijing 102206，China
  • Received:2023-11-20 Online:2024-06-01 Published:2024-07-23
  • Contact: Ming-jun ZHANG E-mail:xiexianyi@ysu.edu.cn;zhangmingjun@catarc.ac.cn

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摘要：

为提高智能汽车在避障换道过程中的舒适度与安全性，提出了一种考虑舒适度的智能汽车人工蜂群轨迹规划方法。通过采样法在速度-时间空间内进行位置采样，得到基于时间的速度序列，并设计新的蜜源搜索策略和蜜源更新策略以提高人工蜂群算法的搜索精度和收敛速度。结合五次多项式拟合速度序列与蜜源位置，得到换道轨迹。考虑车辆加速度和冲击度对舒适度的影响，设计轨迹舒适度评价函数并融合到适应度函数中，通过碰撞检测结果优化换道轨迹。基于Simulink-PreScan-CarSim联合仿真平台验证算法有效性。结果表明：面对不同工况，本文提出方法能够规划出符合加速度、冲击度最值约束的无碰撞安全换道轨迹，且规划结果优于未考虑舒适度的规划轨迹。在实车测试中，本文所提方法在低速场景下能够实现对静态障碍物、低速动态障碍物的避障轨迹规划，避障轨迹均符合舒适度要求，且表现出较好的可跟踪性。

关键词: 车辆工程, 智能汽车, 轨迹规划, 舒适度评价, 人工蜂群, 速度规划, 安全碰撞检测

Abstract:

To enhance the comfort and safety of intelligent vehicles during obstacle avoidance and lane changing， a trajectory planning method was proposed using an artificial bee colony algorithm that considers comfort. By sampling positions in the speed-time space， a time-based speed sequence was obtained. New honey source search and update strategies were designed to improve the search accuracy and convergence speed of the artificial bee colony algorithm. The lane-changing trajectory was derived by combining a fifth-order polynomial fit of the speed sequence with honey source positions. Taking into account vehicle acceleration and jerk for comfort， a trajectory comfort evaluation function was designed and integrated into the fitness function to optimize the lane-changing trajectory based on collision detection results. The effectiveness of the algorithm was validated using the Simulink-PreScan-CarSim jointsimulation platform. The results show that under various conditions， the proposed method can plan collision-free lane-changing trajectories that comply with acceleration and jerk constraints， outperforming trajectories planned without considering comfort. In real vehicle tests， the proposed method can achieve obstacle avoidance trajectory planning for static obstacles and low-speed moving obstacles in low-speed scenarios， with all trajectories meeting comfort requirements and demonstrating good trackability.

Key words: vehicle engineering, intelligent vehicle, trajectory planning, comfort evaluation, artificial bee colony, velocity planning, safety collision detection

中图分类号: 

  • U461.1

图1

优化前后单次迭代时间对比"

图2

优化前后迭代次数对比"

图3

碰撞危险区域示意图"

图4

工况1示意图"

图5

工况1仿真结果"

图6

工况1轨迹数据"

表1

工况1轨迹数据对比"

考虑舒适度未考虑舒适度
纵向加速度最值/（m?s-2）2.317 32.371 6
横向加速度最值/（m?s-2）2.444 02.257 0
纵向冲击度最值/（m?s-3）-4.172 8-3.862 5
横向冲击度最值/（m?s-3）6.231 17.369 1
纵向加速度方差0.313 10.352 8
横向加速度方差1.981 32.098 1
纵向冲击度方差0.943 91.087 2
横向冲击度方差4.527 06.574 4
舒适度函数值2.135 52.366 4
适应度函数值1.455 01.547 4

图7

工况2示意图"

图8

工况2仿真结果"

图9

工况2轨迹数据"

表2

工况2轨迹数据对比"

考虑舒适度未考虑舒适度
纵向加速度最值/（m?s-2）1.964 63.071 3
横向加速度最值/（m?s-2）-2.363 12.244 1
纵向冲击度最值/（m?s-3）-2.422 26.049 1
横向冲击度最值/（m?s-3）5.577 87.777 8
纵向加速度方差1.747 92.478 4
横向加速度方差2.123 32.592 6
纵向冲击度方差1.161 13.610 2
横向冲击度方差4.591 212.463 3
舒适度函数值2.014 13.410 7
适应度函数值1.406 91.965 5

图10

实车测试平台"

图11

静态避障变道过程"

图12

静态避障实车测试结果"

图13

动态避障变道过程"

图14

动态避障实车测试结果"

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