Estimation of tire-road peak adhesion coefficient for intelligent electric vehicles based on camera and tire dynamics information fusion

Leng, Bo; Jin, Da; Liu, Xiong; Yang, Xing; Yu, Zhuoping

doi:10.1016/j.ymssp.2020.107275

Cited by 70 publications

(31 citation statements)

References 17 publications

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“…Road conditions sensing with image data is verified to be feasible in providing essential information for vehicle control systems [4 , 5] . Most existing public datasets contain images collected under limited working conditions, which restrict the robustness of the algorithms in practical applications.…”

Section: Data Descriptionmentioning

confidence: 97%

A road surface image dataset with detailed annotations for driving assistance applications

Zhao

Wei

2022

Data in Brief

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Section: Data Descriptionmentioning

confidence: 97%

A road surface image dataset with detailed annotations for driving assistance applications

Zhao

Wei

2022

Data in Brief

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“…These estimations are then used to calculate the longitudinal slip on each wheel and for the model used by the

controller. To simplify the algorithm, the TRFC is supposed to be obtained by some estimation method [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] and the optimal tire slip that maximizes the braking force is calculated by means of the Burckhardt friction model. Finally, the

controller generates the necessary braking pressure for each wheel in order to minimize the error between the optimal and current longitudinal slip.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The main contributions are: The proposed controller is able to make the tire slip follow a given reference based on the TRFC, resulting in a small reference error and good transient response, guaranteeing system stability. Since the estimation of the TRFC is not the focus of this article, it is assumed to be known for making use of any of the most recent literature algorithms [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. The braking forces are maximized depending on road condition.…”

Section: Introductionmentioning

confidence: 99%

Tire Slip H∞ Control for Optimal Braking Depending on Road Condition

Meléndez-Useros

Jiménez-Salas

Viadero-Monasterio

et al. 2023

Sensors

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Tire slip control is one of the most critical topics in vehicle dynamics control, being the basis of systems such the Anti-lock Braking System (ABS), Traction Control System (TCS) or Electronic Stability Program (ESP). The highly nonlinear behavior of tire–road contact makes it challenging to design robust controllers able to find a dynamic stable solution in different working conditions. Furthermore, road conditions greatly affect the braking performance of vehicles, being lower on slippery roads than on roads with a high tire friction coefficient. For this reason, by knowing the value of this coefficient, it is possible to change the slip ratio tracking reference of the tires in order to obtain the optimal braking performance. In this paper, an H∞ controller is proposed to deal with the tire slip control problem and maximize the braking forces depending on the road condition. Simulations are carried out in the vehicular dynamics simulator software CarSim. The proposed controller is able to make the tire slip follow a given reference based on the friction coefficient for the different tested road conditions, resulting in a small reference error and good transient response.

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“…Under extreme conditions, the active front wheel steering system can improve the stability of the vehicle by appropriately modifying the front wheel steering angle according to the running state of the vehicle and the driver's intention. In this paper, the vehicle centroid equation of active front wheel steering control based on yaw stability is established [27], as shown in Equation (18).…”

Section: Vehicle Active Front Wheel Steering Modelmentioning

confidence: 99%

Multi-Agent-Based Coordinated Control of ABS and AFS for Distributed Drive Electric Vehicles

Zhang

Wang

et al. 2022

Energies

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A vehicle with a four-channel anti-lock braking system (ABS) has poor safety and stability when braking on a low-adhesion road or off-road. In view of this situation, this paper proposes a multi-objective optimization coordinated control method for ABS and AFS based on multi-agent model predictive control (MPC). Firstly, the single-wheel control method is adopted to establish the single-wheel equation based on the slip rate and the stability equation of the centroid yaw based on AFS. The four wheels and the centroid are regarded as agents. The mathematical model of distributed drive electric vehicles based on graph theory and the coordinated control of AFS and ABS is established to reduce the dimension of the model. Secondly, on the basis of the multi-agent theory, an integrated coordinated control method for AFS and ABS based on distributed model predictive control (DMPC) is proposed to realize the ideal values of the vehicle’s slip rate, yaw rate, and sideslip angle, and improve the braking safety and handling stability of the vehicle. Then, to solve the problems of high levels of resource consumption, low real-time performance, and complex implementation in the optimization of the DMPC solution, a prediction solution method using a discrete simplified dual neural network (SDNN) is proposed to balance the computational efficiency and system dynamic performance. Finally, a hardware-in-the-loop (HIL) test bench is built to test the effectiveness of the proposed method under the conditions of a low-adhesion road and an off-road.

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Estimation of tire-road peak adhesion coefficient for intelligent electric vehicles based on camera and tire dynamics information fusion

Cited by 70 publications

References 17 publications

A road surface image dataset with detailed annotations for driving assistance applications

A road surface image dataset with detailed annotations for driving assistance applications

Tire Slip H∞ Control for Optimal Braking Depending on Road Condition

Multi-Agent-Based Coordinated Control of ABS and AFS for Distributed Drive Electric Vehicles

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