Trajectory research of Cellular Automaton Model based on real driving behaviour

Xianyan Kuang and Ziru Chen

Physica A: Statistical Mechanics and its Applications, 2022, vol. 602, issue C

Abstract: Realistic trajectory research remains a challenging problem in the traffic simulation of the Cellular Automaton Model. The lane changing angle and trajectory of vehicles change dynamically which was less considered in the previous CA models. This paper focuses on the lane changing process of the CA Model based on real driving behaviour, and we established a modified CA model (TN-CA) for trajectory research based on the NaSch model. Firstly, we propose a new approach for straight and curved roads based on Frenet Frame, this allows the CA model to be applied in a more realistic road scenario. Secondly, we propose a novel trajectory research cell grids generation method for the lane changing process to reproduce the realistic trajectory of vehicles in the TN-CA model, and this approach was called the TN-Cell grids generation method. Thirdly, we developed real-time lane detection tools and a real driving simulator based on our TN-CA model and used the collected data for comparative analysis. The experimental results show that our TN-CA model can better reproduce the dynamic characteristics of lane changing angle and speed in the lane changing process. A video of the TN-CA model and its application is shown at https://www.youtube.com/watch?v=LOejS5EO6Xk.

Keywords: Trajectory research; Cellular Automaton Model; Lane changing; Real-time lane detection; Real driving simulator (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:602:y:2022:i:c:s0378437122004162

DOI: 10.1016/j.physa.2022.127610

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