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Effect analysis of CAV lane-changing trajectory planning strategies based on fine grained cellular automaton model

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  • Shuhui, Zheng
  • Hui, Shen
  • Guorong, Zheng
  • Xiaoming, Liu

Abstract

Currently, a thorough analysis about how the lane-changing (LC) trajectory planning strategies of Connected Autonomous Vehicles (CAVs) affect human-machine hybrid traffic flow is still lack. In response to this issue, firstly, a Fine Grained Cellular Automaton (FGCA) traffic flow model which can characterize the LC trajectory planning strategies of CAVs was proposed. Then, considering LC turning back phenomenon, the LC intention model was presented based on collision risk. Furthermore, three different LC trajectory planning strategies for CAV were designed with FGCA which can map the real LC trajectory characteristics such as LC target position, LC duration and LC turning back. Finally, simulation analysis was conducted for these strategies. The results showed that in human-machine hybrid traffic flow environment, each of three strategies may be outstanding in certain metrics. It is possible to enhance the traffic flow operation efficiency by guiding all CAVs to select appropriate LC trajectory planning strategy, and this selection can be based on factors such as traffic flow density, CAVs penetration rate, etc.

Suggested Citation

  • Shuhui, Zheng & Hui, Shen & Guorong, Zheng & Xiaoming, Liu, 2025. "Effect analysis of CAV lane-changing trajectory planning strategies based on fine grained cellular automaton model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 676(C).
    • Handle: RePEc:eee:phsmap:v:676:y:2025:i:c:s0378437125005370
    DOI: 10.1016/j.physa.2025.130885
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