Volume 22 Issue 3
Jun.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 89-103
WANG Ping, QI Xu-dong, YANG Jing-wen, HAN Yu, LI Li, WANG Gui-ping, YAO Jun-feng, ZHAO Xiang-mo. Left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 89-103. doi: 10.19818/j.cnki.1671-1637.2022.03.007
Citation: WANG Ping, QI Xu-dong, YANG Jing-wen, HAN Yu, LI Li, WANG Gui-ping, YAO Jun-feng, ZHAO Xiang-mo. Left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 89-103. doi: 10.19818/j.cnki.1671-1637.2022.03.007
shu

Left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict

doi: 10.19818/j.cnki.1671-1637.2022.03.007
  • 1.

    School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China

  • 3.

    Guangdong Key Laboratory of Fire Science and Intelligent Emergency Technology, Sun Yat-Sen University, Guangzhou 528406, Guangdong, China

  • 4.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 5.

    School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, Shaanxi, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

National Natural Science Foundation of China 71901040

More Information
  • Author Bio:

    WANG Ping(1982-), female, professor, PhD, wang0372@e.ntu.edu.sg

    QI Xu-dong(1998-), male, doctoral student, xdqi@chd.edu.cn

  • Received Date: 2021-12-14
  • Publish Date: 2022-06-25
    Abstract
  • In order to improve the driving safety, reliability and efficiency of autonomous vehicles at complex vehicle-non-motor mixed intersections, a left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict was proposed and generalized. The division rules of static discrete sequence intersection grid area were set.According to the probability conversion relationship of traffic behaviors in multi traffic states, the motion state of non-motor vehicle in the subdivision grid was predicted, and the risk level of the conflict area between motor vehicles and non-motor vehicles was dynamically evaluated. On this basis, the model prediction method was used to design the lateral and longitudinal control algorithms of autonomous vehicle, the desired trajectory could be tracked by adaptively adjusting the heading and speed, and the grid conflict area could be avoided synchronously. Combined with the constraints of vehicle dynamics and external interaction environment, a traffic simulation platform of intersection four-phase signal control was developed. From the aspects of efficiency optimization, comfort optimization, and the offset between actual planned path and reference path, the model-in-the-loop test was used to verify the effectiveness of the left-turn motion planning in the vehicle-non-motor conflict area. Research results show that the proposed model can effectively extract and predict grid risk information dynamically, and improves the safe interaction, efficient traffic and driving comfort between autonomous vehicles and surrounding non-motor vehicles. Compared with similar algorithms, the offset of the planned path can reduce by 17.1%, the traffic efficiency can increase by 26.6% and the comfort can increase by 39.3% at most. Therefore, the proposed model has obvious advantages in the efficient passage through vehicle-non-motor conflict area and low space occupation by the planned path. 2 tabs, 14 figs, 32 refs.

     

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