Multiple-Vehicle Longitudinal Collision Mitigation by Coordinated Brake Control

Lu, Xiao‐Yun; Wang, Jianqiang; Li, Shengbo Eben; Zheng, Yang

doi:10.1155/2014/192175

Cited by 16 publications

(13 citation statements)

References 10 publications

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“…Vehicle mass (kg) is assumed to conform to

U false(1100, 1700 false)

. Similar to [36], we also assume a linear relation between vehicle length and vehicle mass, as well as between the vehicle's maximum deceleration and the vehicle mass. Precisely, the vehicle length (m) varies from 3.5 to 5 and the maximum deceleration (m/s 2 ) varies from 5.5 to 6.5.…”

Section: Simulation Methodology and Designmentioning

confidence: 99%

“…Driver desired speed (km/h) is assumed to accord with the uniform distribution of

U false(20, 30 false)

. We refer to [36] and choose a driver desired time headway (THW, s) for car‐following to be

N ()(, {1.5, 0.1}^{2})

. The driving direction of each driver is considered as an invariable random choice among left‐turning, straight‐crossing, and right‐turning with the same probability.…”

Section: Simulation Methodology and Designmentioning

confidence: 99%

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Vehicle safety analysis at non‐signalised intersections at different penetration rates of collision warning systems

Liu

Wang

Chen

et al. 2020

IET intell. transp. syst

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The market penetration of intelligent vehicles is a long‐term process. In recent years, significant attention has been paid to the influence of technology market penetration rate (MPR) on efficiency at signalised intersections, but little attention has been given to such an influence on safety at non‐signalised intersections. In this study, the influence of the intersection collision warning (ICW) system MPR on safety at non‐signalised intersections is investigated. The authors built a Matlab‐based simulation platform where an ICW algorithm was implemented. The simulation was firstly conducted to verify their models. Then the simulation results of different MPRs were obtained and compared statistically. Collision probability, conflict index, and collision rate were analysed for safety evaluation. The overall results showed that vehicle safety at non‐signalised intersections improves with the increase of the ICW system MPR. Without considerations of inappropriateness and otherness of driver reaction to warnings, when the MPR is 20% and all vehicles are connected by vehicle‐to‐everything, the collision probability, conflict index, and collision rate can be reduced by around 20, 20, and 35%, respectively. The simulation method can establish a mapping relation between the ICW system MPRs and vehicle safety indices at non‐signalised intersections.

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“…Vehicle mass (kg) is assumed to conform to

U false(1100, 1700 false)

Section: Simulation Methodology and Designmentioning

confidence: 99%

“…Driver desired speed (km/h) is assumed to accord with the uniform distribution of

U false(20, 30 false)

. We refer to [36] and choose a driver desired time headway (THW, s) for car‐following to be

N ()(, {1.5, 0.1}^{2})

. The driving direction of each driver is considered as an invariable random choice among left‐turning, straight‐crossing, and right‐turning with the same probability.…”

Section: Simulation Methodology and Designmentioning

confidence: 99%

Vehicle safety analysis at non‐signalised intersections at different penetration rates of collision warning systems

Liu

Wang

Chen

et al. 2020

IET intell. transp. syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the process of vehicle motion and vehicle longitudinal dynamics model simulation, k t is a real-time observation. Substitute equation (40) into equation (39), and get:…”

Section: Inverse Vehicle Longitudinal Dynamicmentioning

confidence: 99%

“…After the switching between engine torque output and brake control, if the system is switched to the brake control, the desired braking pressure needs to be calculated according to the desired acceleration [38], [39]. During this period, the inverse braking system model needs to be established.…”

Section: Inverse Vehicle Longitudinal Dynamicmentioning

confidence: 99%

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

et al. 2019

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Longitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dynamics, such as large velocity tracking errors, strong chattering phenomenon and so on, a new sliding mode control strategy based on RBF (Radical Basis Function) neural network is presented in this paper. Firstly, a nonlinear mathematical model of the intelligent vehicle longitudinal motion is established by considering the dynamics of the engine, the torque converter, the automatic transmission and the brake system. On the basis of the system model, a variable structure control system with sliding mode is introduced to design a sliding mode variable controller with RBF neural network. This controller can adaptively adjust the switching gain and its stability is proved based on the Lyapunov theory. Finally, the effectiveness of the designed longitudinal velocity control strategy is verified by simulation under typical driving conditions. The simulation results show that the improved control algorithm can effectively suppress chattering, obtain the higher precision and stronger robustness than the traditional sliding mode control. Thus, the longitudinal motion control performance of intelligent vehicles is improved effectively. INDEX TERMS Intelligent vehicles, intelligent transportation system, longitudinal dynamics control, sliding mode control, RBF neural network.

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“…The algorithm can identify the target and judge the safety status, and provide early warning or active braking intervention for different danger levels. Lu et al 15 developed a centralized anti-collision control algorithm based on vehicle communication technology. This method is applied to the estimate of vehicle collision energy.…”

Section: Introductionmentioning

confidence: 99%

Safety distance model for longitudinal collision avoidance of logistics vehicles considering slope and road adhesion coefficient

Guo

Wang

Lili

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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With the acceleration of urbanization process, the country’s strong support for the healthy development of the logistics industry has made urban logistics become a hot topic in recent years. With the increase in the number of logistics vehicles, traffic accidents have become more frequent. Intelligent vehicle collision avoidance system is an important part of advanced safety technology. To increase veracity and practicability of logistics vehicle safety collision avoidance, this paper presents a safety distance model for longitudinal collision avoidance of logistics vehicles considering road slope and road adhesion coefficient. Based on the vehicle kinetic theory, the information of surrounding environment for the vehicle is obtained using environment sensing system adequately, a method is designed to estimate the road slope and road adhesion coefficient. Combined with vehicle dynamics and tire normal force variation, the road slope was estimated. Based on the relationship between slip rate and adhesion coefficient, the Least Square Method is used for multivariate fitting to obtain the relationship between rolling resistance coefficient and road adhesion coefficient, estimate the road adhesion coefficient, and the maximum deceleration of vehicle braking is modified. Therefore, the safety distance model is established. In order to verify the accuracy and effectiveness of the model, three cases are designed for verification: case I is verification of the safety distance model considering the slope factor; case II is verification of the safety distance model considering the road adhesion factor; case III is the safety distance verification considering both the factors of slope and road adhesion coefficient. The result shows that it is necessary to take the factors of road slope and adhesion coefficient into the safety distance model to improve the accuracy of the model.

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Multiple-Vehicle Longitudinal Collision Mitigation by Coordinated Brake Control

Cited by 16 publications

References 10 publications

Vehicle safety analysis at non‐signalised intersections at different penetration rates of collision warning systems

Vehicle safety analysis at non‐signalised intersections at different penetration rates of collision warning systems

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

Safety distance model for longitudinal collision avoidance of logistics vehicles considering slope and road adhesion coefficient

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