Volume 21 Issue 2
Aug.  2021
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ZHAO Shu-en, WANG Jin-xiang, LI Yu-ling. Lane changing trajectory planning of intelligent vehicle based on multiple objective optimization[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 232-242. doi: 10.19818/j.cnki.1671-1637.2021.02.020
Citation: ZHAO Shu-en, WANG Jin-xiang, LI Yu-ling. Lane changing trajectory planning of intelligent vehicle based on multiple objective optimization[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 232-242. doi: 10.19818/j.cnki.1671-1637.2021.02.020
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Lane changing trajectory planning of intelligent vehicle based on multiple objective optimization

doi: 10.19818/j.cnki.1671-1637.2021.02.020

  • School of Mechanotronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Natural Science Foundation of China 52072054

Natural Science Foundation of Chongqing cstc2018jcyjAX0422

More Information
  • Author Bio:

    ZHAO Shu-en(1972-), male, professor, PhD, zse0916@163.com

  • Corresponding author: WANG Jin-xiang(1993-), male, graduate student, 383148322@qq.com
  • Received Date: 2020-10-22
  • Publish Date: 2021-04-25
    Abstract
  • To improve the anthropomorphism and real-time performance of lane changing trajectory planning for intelligent vehicles, a lane changing trajectory planning algorithm based on the multi-objective collaborative optimization of safety, comfort, and energy saving was proposed. The adaptation of proposed trajectory planning method depended on the constraints of key variables such as lane changing time, longitudinal and lateral velocities, and accelerations. Based on the theory of vehicle kinematics and dynamics, the safe area of vehicle lane changing in dynamic unknown environments was analyzed, and the ideal lane-changing trajectory model of a sixth-degree polynomial was established. A genetic algorithm-back propagation neural network was used to predict the end time and target position of lane changing, and lane changing trajectory clusters in complex scenes were obtained. The performance evaluation functions of safety, comfort, and economy of vehicle lane changing based on feasible solution space were analyzed, and the objective function and constraint conditions of multi-objective collaborative optimization were constructed. The whale optimization algorithm was used to optimize the lane changing trajectory clusters to achieve an optimal lane changing trajectory planning of intelligent vehicles with multi-performance objectives. To further verify the accuracy of the multi-objective optimization trajectory planning algorithm, an L3-level intelligent vehicle test platform was used to test the algorithm for intelligent vehicles in structured road scenes. Simulation and experimental results show that the proposed algorithm can successfully achieve smooth and safe lane changing under various constraints. Compared with traditional lane changing of driver, the safety, comfort, and multi-objective comprehensive performance of the method are improved by 5.1%, 3.3%, and 1.7%, respectively, which effectively improves the personification of intelligent vehicle lane-changing trajectory planning in dynamic environments. 2 tabs, 11 figs, 30 refs.

     

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