Citation

Cong S, Wang W, Liang J, Chen L, Cai Y. IEEE Trans. Intel. Transp. Syst. 2022; 23(7): 8477-8487.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2021.3082944

PMID

unavailable

Abstract

This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output the pre-control parameters. Secondly, the back propagation neural network avoidance model, which combined with driver's avoidance behavior, is developed for achieving the instantaneous collision avoidance control. Moreover, the optimal solution between control parameters and vehicle stability is obtained by using linear quadratic regulator. Finally, the accuracy of the avoidance model is verified by the semi-physical driver-in-the-loop simulation based on PreScan/Simulink.

RESULTS show that the automatic vehicle with the proposed avoidance model can accurately and effectively take pre-braking and micro-steering behavior. The proposed model can greatly reduce vehicle collision probability and effectively take both safety and comfort of collision avoidance into account. In addition, the robustness of the control model under different network penetration is discussed.

Language: en

Keywords

back propagation neural network; collision avoidance; Collision avoidance; dangerous lane-changing probability; hidden Markov model; Hidden Markov models; mixed connected vehicle; Neural networks; Predictive models; Stability criteria; Traffic safety; Trajectory; Wheels