Stability analysis of a general nonlinear car-following model

Du, Wenju; Li, Yinzhen; Zhang, Jiangang; Jiang, Yu

doi:10.1080/00207179.2018.1514128

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…In recent years, there's is increase in traffic day by day. It causing more congestion, pollution, time wasted and accidents Because of this, many research projects have started, offering different mathematical modelling to help to solve these problems [1,2,3,4,5,6,7] additionally number of mathematical car-following models [8,9,10,11,12] given by Wang, Chen, Zhang etc. and continuum models have been proposed by Yu [13],Mohan [14], Gupta [15],Jiang [16] Besson [17] etc.…”

Section: Introductionmentioning

confidence: 99%

Investigating The Influence Of Driver Misconceptions On Road Behaviour Using A Novel Lattice Hydrodynamic Model

Mehra,

Sharma

2024

eatp

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The misconception of drivers' behaviour on the road has a significant impact on traffic affecting other drivers and leading to traffic jams and accidents. Consequently, a new hydrodynamic model has been developing for traffic flow to investigate the effects of misconception of drivers' behaviour on the road. Through Density-sensitivity phase analysis it has been observed that the stable region of traffic flow expands as the misunderstanding of drivers decreases. Moreover, through nonlinear analysis, a kink-antikink soliton solution of the mKdV equation associated with the misconception effect has been identified. Finally, numerical simulations were executed to analyse the evolution of density and flux. The results and through differed graphs indicate that, as the misconception of drivers behaviour diminishes traffic flow becomes increasingly stable.

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Section: Introductionmentioning

confidence: 99%

Investigating The Influence Of Driver Misconceptions On Road Behaviour Using A Novel Lattice Hydrodynamic Model

Mehra,

Sharma

2024

eatp

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“…In recent years, many scholars have studied the car-following model. [1][2][3][4][5][6][7][8][9][10] However, all the above studies just consider how to improve the stability of traffic flow and restrain traffic jam under the condition of single lane. Compared with the single lane, the vehicle running in the two-lane is more complicated, not only has the interference from the front car in the driveway, but also is squeezed by the lateral vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…The car‐following model regards the vehicle in the traffic flow as a dispersed particle, and the characteristics of the single‐lane traffic flow can be understood by observing the single vehicle following the vehicle in front of the vehicle in the case of no overtaking. In recent years, many scholars have studied the car‐following model 1–10 …”

Section: Introductionmentioning

confidence: 99%

Stability control of a two‐lane car‐following model based on cluster synchronization of complex network

Du,

Li,

Zhang

2023

Optim Control Appl Methods

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The article investigated the stability of a two‐lane car‐following model with lateral friction on the basis of cluster synchronization theory of complex network. By using the Lyapunov stability theory and designing the appropriate controller, the two‐lane car‐following model with lateral friction is quickly stabilized and the stability condition of the model is obtained. Besides, based on the adaptive cluster synchronization theory for complex networks with external disturbances, the stability of two‐lane car‐following model with lateral friction is studied when the vehicles is subjected to random external disturbance. Finally, the numerical simulation is carried out by using Matlab simulation technology, the results show that the two‐lane car‐following model with lateral friction is rapidly stabilizing and congestion phenomenon is effectively alleviated under the controller designed.

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“…Recent decades have witnessed an increasing attention on the integration of autonomous vehicles and artificial intelligence technology, toward achieving an intelligent transport system, such as the autonomous ground vehicle (AGV) (Fényes et al, 2021; Xia et al, 2020; Xie et al, 2021b). This is because intelligent transport system could improve vehicle safety and road utilization, and could also alleviate traffic congestion (Du et al, 2020; Fernandez et al, 2020; Julius Fusic et al, 2021). The in-wheel motors of distributed drive vehicles have been proven to be able to produce accurate, flexible and fast torque response for each driven wheel (Jin et al, 2018; Li et al, 2021a; Sato et al, 2015), demonstrating the potentiality of distributed drive vehicle in improving vehicle handling, flexibility and safety (Xin et al, 2017; Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Adaptive coordination control strategy for path-following of DDAEV with neural network based moving weight coefficients

Xie

Wang

et al. 2022

Transactions of the Institute of Measurement and Control

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This paper presents an adaptive coordination control strategy for path-following of distributed drive autonomous electric vehicles (DDAEV). A model predictive control (MPC) algorithm is used to realize path-following through autonomous steering, where the prediction time is adaptive in relation to different driving conditions. Due to the dynamic characteristic of distributed drive vehicle, the differential torque control is also utilized based on the deviation of path to realize path-following. In order to make full use of the advantages of these two path-following methods, coordination control of autonomous steering and differential steering is adopted to improve the transient response speed, flexibility of steering system and path-following performance by setting moving weight coefficients based on neural network. Results of CarSim-Simulink co-simulation and real vehicle experiment both verify that the proposed coordination control can obtain steering flexibility, as well as tracking accuracy and reliability under various driving conditions.

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Stability analysis of a general nonlinear car-following model

Cited by 6 publications

References 28 publications

Investigating The Influence Of Driver Misconceptions On Road Behaviour Using A Novel Lattice Hydrodynamic Model

Investigating The Influence Of Driver Misconceptions On Road Behaviour Using A Novel Lattice Hydrodynamic Model

Stability control of a two‐lane car‐following model based on cluster synchronization of complex network

Adaptive coordination control strategy for path-following of DDAEV with neural network based moving weight coefficients

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