Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (2): 738-746.doi: 10.13229/j.cnki.jdxbgxb20191201

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A vehicle collision avoidance control method based on model predictive composite control

Shou-tao LI1,2(),Rui WANG2,Jing-chun XU2,De-jun WANG1,2,Yan-tao TIAN1,2,Ding-li YU3   

  1. 1.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
    2.College of Communication Engineering，Jilin University，Changchun 130022，China
    3.School of Engineering and Technology，Liverpool John Moores University，Liverpool L33AF，UK
  • Received:2020-01-03 Online:2021-03-01 Published:2021-02-09

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Abstract:

In order to reduce the increasing traffic safety problems， the development of vehicle collision early warning and collision avoidance control system is essential. This paper presents a compound control and collision avoidance method based on the longitudinal and lateral dynamics of vehicles to reduce traffic accidents. First， the vehicle inverse longitudinal and lateral dynamics models are established respectively. The longitudinal controller judges whether the vehicle is in a dangerous state and gives collision warning through the safety distance model. Then， the upper model prediction controller and the bottom single neuron PID controller are designed by using the layered control method. Finally， the model predictive controller is designed by combining the parameters constraint at different speeds. Simulation experiments under different working conditions were performed， and results show that the control system designed in this paper can avoid collision successfully and improve the safety， stability and comfort of the vehicle.

Key words: vehicle engineering, active vehicle safety, vehicle collision avoidance, safety distance model, longitudinal control, lateral lane change

CLC Number: 

  • TP273

Fig.1

Vehicle collision avoidance system"

Fig.2

Diagram of transverse force"

Table 1

Road adhesion coefficient in different environments"

路面类型路面附着系数
干燥潮湿
水泥0.60~0.750.45~0.65
沥青0.55~0.700.45~0.65
冰雪0.20~0.35-

Fig.3

Control structure block diagram of single neuron PID"

Fig.4

Acceleration after deceleration of front car"

Fig.5

Emergency braking at different speeds"

Fig.6

Contrast of vehicle controller effect at 90 km/h"

Fig.7

Trajectory distance at different initial distances"

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