Improvement of active safety systems by the extended Kalman filter based estimation of tire-road friction coefficient

Enisz, Krisztián; Fodor, D.; Szalay, István; Kohlrusz, Gábor

doi:10.1109/ievc.2014.7056186

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Effectbased method was presented by measuring the related response of vehicle dynamics model and applies extended kalman filtering or other algorithm to obtain its value. The vehicle dynamics model included both longitudinal and/or lateral dynamics [16,17]. The main features of these methods could make full use of the on-board sensors and reduce costs, which has been widely used.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Road Friction Coefficient in Different Road Conditions Based on Vehicle Braking Dynamics

Zhao

Lin

et al. 2017

Chin. J. Mech. Eng.

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The accurate estimation of road friction coefficient in the active safety control system has become increasingly prominent. Most previous studies on road friction estimation have only used vehicle longitudinal or lateral dynamics and often ignored the load transfer, which tends to cause inaccurate of the actual road friction coefficient. A novel method considering load transfer of front and rear axles is proposed to estimate road friction coefficient based on braking dynamic model of two-wheeled vehicle. Sliding mode control technique is used to build the ideal braking torque controller, which control target is to control the actual wheel slip ratio of front and rear wheels tracking the ideal wheel slip ratio. In order to eliminate the chattering problem of the sliding mode controller, integral switching surface is used to design the sliding mode surface. A second order linear extended state observer is designed to observe road friction coefficient based on wheel speed and braking torque of front and rear wheels. The proposed road friction coefficient estimation schemes are evaluated by simulation in ADAMS/Car. The results show that the estimated values can well agree with the actual values in different road conditions. The observer can estimate road friction coefficient exactly in real-time and resist external disturbance. The proposed research provides a novel method to estimate road friction coefficient with strong robustness and more accurate.

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Section: Introductionmentioning

confidence: 99%

Estimation of Road Friction Coefficient in Different Road Conditions Based on Vehicle Braking Dynamics

Zhao

Lin

et al. 2017

Chin. J. Mech. Eng.

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“…normal, random variable. Consequently, several algorithms available in the literature, such as those presented in [142][143][144][145][146] can be adopted for implementing the cooperative strategy in [141]. Usually, when Kalman strategy are used, the road friction coefficient is estimated together with other measures for vehicle dynamics such as vehicle sideslip angle, wheel sideslip angles and wheel slip ratios, and forces acting on tyres.…”

Section: Road Friction Estimationmentioning

confidence: 99%

“…Consequently, more detailed vehicle models compared to those discussed in Sections 2 and 3 are designed for reproducing the vehicle motion while capturing tyre dynamics. For instance, in [143] the second-order nonlinear longitudinal vehicle model discussed in Section 2 is augmented with the dynamics of the wheels, i.e., the wheel's angular velocity and the wheel's longitudinal slip, which in turn provide the longitudinal tyre force in accordance to the Pacejka model. Moreover, detailed lateral vehicle dynamics for the road friction estimation via Kaman filtering strategies has been considered in [142,[144][145][146].…”

Section: Road Friction Estimationmentioning

confidence: 99%

Towards connected autonomous driving: review of use-cases

Montanaro

Dixit

Fallah

et al. 2018

Vehicle System Dynamics

140

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Connected autonomous vehicles are considered as mitigators of issues such as traffic congestion, road safety, inefficient fuel consumption, and pollutant emissions that current road transportation system suffers from. Connected Autonomous vehicles utilise communication systems to enhance the performance of autonomous vehicles and consequently improve transportation by enabling cooperative functionalities, namely, cooperative sensing and cooperative manoeuvring. The former refers to the ability to share and fuse information gathered from vehicle sensors and road infrastructures to create a better understanding of the surrounding environment while the latter enables groups of vehicles to drive in a co-ordinated way which ultimately results in a safer and more efficient driving environment. However, there is a gap in understanding how and to what extent connectivity can contribute in improving the efficiency, safety and performance of autonomous vehicles. Therefore, the aim of this paper is to investigate the potential benefits that can be achieved from connected autonomous vehicles through analysing five use-cases: (i)vehicle platooning, (ii) lane changing, (iii) intersection management, (iv) intersection management, and (v) road friction estimation. The current paper highlights that although connectivity can enhance the performance of autonomous vehicles and contribute to improvement of current transportation system performance, the level of achievable benefits depends on factors such as the penetration rate of connected vehicles, the number of autonomous vehicles, traffic scenarios, and the way of augmenting off-board information into vehicle control systems.

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“…Wenzel et al [10] proposed an estimation method based on a double extended Kalman filter (DEKF). Enisz et al [11] used an extended Kalman filter (EKF) method to estimate the instantaneous and maximum values of road friction coefficient. Zhao et al [12] designed an EKF method based on a braking dynamics model considering the load transfer of front and rear axles.…”

Section: Introductionmentioning

confidence: 99%

Research on Curved Path Tracking Control for Four-Wheel Steering Vehicle considering Road Adhesion Coefficient

Liu

Wei

Sang

et al. 2020

Mathematical Problems in Engineering

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Curved path tracking control is one of the most important functions of autonomous vehicles. First, small turning radius circular bends considering bend quadrant and travel direction restrictions are planned by polar coordinate equations. Second, an estimator of a vehicle state parameter and road adhesion coefficient based on an extended Kalman filter is designed. To improve the convenience and accuracy of the estimator, the combined slip theory, trigonometric function group fitting, and cubic spline interpolation are used to estimate the longitudinal and lateral forces of the tire model (215/55 R17). Third, to minimize the lateral displacement and yaw angle tracking errors of a four-wheel steering (4WS) vehicle, the front-wheel steering angle of the 4WS vehicle is corrected by a model predictive control (MPC) feed-back controller. Finally, CarSim® simulation results show that the 4WS autonomous vehicle based on the MPC feed-back controller can not only significantly improve the curved path tracking performance but also effectively reduce the probability of drifting or rushing out of the runway at high speeds and on low-adhesion roads.

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Improvement of active safety systems by the extended Kalman filter based estimation of tire-road friction coefficient

Cited by 4 publications

References 10 publications

Estimation of Road Friction Coefficient in Different Road Conditions Based on Vehicle Braking Dynamics

Estimation of Road Friction Coefficient in Different Road Conditions Based on Vehicle Braking Dynamics

Towards connected autonomous driving: review of use-cases

Research on Curved Path Tracking Control for Four-Wheel Steering Vehicle considering Road Adhesion Coefficient

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