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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
28.06.2023
DOI
https://doi.org/10.7307/ptt.v35i3.118
ISSUE
2023 (Vol 35), Issue 3
LICENSE
Copyright (c) 2024 Xingyu Chen, Haijian Bai, Heng Ding, Jianshe Gao; Wenjuan Huang
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

A Safety Control Method of Car-Following Trajectory Planning Based on LSTM

Authors:Xingyu Chen, Haijian Bai, Heng Ding, Jianshe Gao; Wenjuan Huang

Abstract

This paper focuses on the potential safety hazards of collision in car-following behaviour generated by deep learning models. Based on an intelligent LSTM model, combined with a Gipps model of safe collision avoidance, a new, Gipps-LSTM model is constructed, which can not only learn the intelligent behaviour of people but also ensure the safety of vehicles. The idea of the Gipps-LSTM model combination is as follows: the concept of a potential collision point (PCP) is introduced, and the LSTM model or Gipps model is controlled and started through a risk judgment algorithm. Dataset 1 and dataset 2 are used to train and simulate the LSTM model and Gipps-LSTM model. The simulation results show that the Gipps-LSTM can solve the problem of partial trajectory collision in the LSTM model simulation. Moreover, the risk level of all trajectories is lower than that of the LSTM model. The safety and stability of the model are verified by multi-vehicle loop simulation and multi-vehicle linear simulation. Compared with the LSTM model, the safety of the Gipps-LSTM model is improved by 42.02%, and the convergence time is reduced by 25 seconds.

Keywords:car-following model, LSTM, Gipps model, safety control, potential collision point

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