Original scientific paper
https://doi.org/10.7307/ptt.v35i3.118
A Safety Control Method of Car-Following Trajectory Planning Based on LSTM
Xingyu Chen
; School of Automotive and Transportation Engineering, Hefei University of Technology
Haijian Bai
; School of Automotive and Transportation Engineering, Hefei University of Technology
Heng Ding
; School of Automotive and Transportation Engineering, Hefei University of Technology
Jianshe Gao
; School of Automotive and Transportation Engineering, Hefei University of Technology
Wenjuan Huang
; School of Automotive and Transportation Engineering, Hefei University of Technology
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
Hrčak ID:
304868
URI
Publication date:
28.6.2023.
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