Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (6): 1570-1581.doi: 10.13229/j.cnki.jdxbgxb.20231280

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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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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

CLC Number: 

  • U461.1

Fig.1

Comparison of single iteration time before and after optimization"

Fig.2

Comparison of iterations before and after optimization"

Fig.3

Collision hazard area diagram"

Fig.4

Schematic diagram of working condition 1"

Fig.5

Simulation results of working condition 1"

Fig.6

Trajectory data of working condition 1"

Table 1

Comparison of trajectory data under working condition 1"

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

Fig.7

Schematic diagram of working condition 2"

Fig.8

Simulation results of working condition 2"

Fig.9

Trajectory data of working condition 2"

Table 2

Comparison of trajectory data under working condition2"

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

Fig.10

Real vehicle test platform"

Fig.11

Static obstacle avoidance lane change process"

Fig.12

Static obstacle avoidance test results"

Fig.13

Dynamic obstacle avoidance and lane change process"

Fig.14

Dynamic obstacle avoidance test results"

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