Testing Technology of Dynamic Road Friction Coefficient

2013 ◽  
Vol 312 ◽  
pp. 249-253 ◽  
Author(s):  
Peng Zhang ◽  
Li Lin Cui ◽  
Le He ◽  
Qun Sheng Xia
Keyword(s):  
Friction Coefficient ◽  
Personal Computer ◽  
Slip Rate ◽  
Test System ◽  
Wheel Speed ◽  
Adhesion Coefficient ◽  
Testing Technology ◽  
Road Friction ◽  
Road Friction Coefficient ◽  
Real Vehicle

The dynamic road adhesion coefficients at 30km/h and 50km/h for dry condition were measured through the real vehicle equipped with a test system driving on the asphalt dry road. The test system included the fifth wheel instrument, the wheel speed sensors, the programmed strain amplifier and ACME portable industrial personal computer. The results show that longitudinal peak adhesion coefficients at 30km/h and 50km/h for the dry condition are about 0.7. Longitudinal adhesion coefficient increases rapidly with the slip rate increasing and soon reaches the maximum. The longitudinal peak adhesion coefficient occures when the slip rate is about 12%.

scholarly journals Calculation Algorithm of Tire-Road Friction Coefficient Based on Limited-Memory Adaptive Extended Kalman Filter

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Bin Huang ◽  
Xiang Fu ◽  
Sen Wu ◽  
Song Huang
Keyword(s):  
Kalman Filter ◽  
Friction Coefficient ◽  
Covariance Matrix ◽  
Extended Kalman Filter ◽  
Estimation Accuracy ◽  
Limited Memory ◽  
Road Friction ◽  
Adaptive Extended Kalman Filter ◽  
Road Friction Coefficient ◽  
Real Vehicle

In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed. By combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old measurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh covariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference to accurately track the breaking status of system. Therefore, problems, including large filter error and divergence caused by incorrect model, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the different road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF results. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation accuracy of algorithm, and increase algorithm robustness.

scholarly journals Study on Pre-Warning Method of the Lateral Security of Heavy Vehicle in Deteriorative Weather

2014 ◽  
Vol 8 (1) ◽  
pp. 292-296
Author(s):  
Zhi-Guo Zhao ◽  
Min Chen ◽  
Nan Chen ◽  
Yong-Bing Zhao ◽  
Xin Chen
Keyword(s):  
Friction Coefficient ◽  
Critical Speed ◽  
Warning System ◽  
Heavy Vehicle ◽  
Heavy Vehicles ◽  
Core Technology ◽  
Road Friction ◽  
Turning Radius ◽  
Safety Features ◽  
Road Friction Coefficient

The lateral security of heavy vehicle in deteriorative weather is one of the main causes of accidents of vehicles on roads. Road safety has become a subject of great concern to institutions of higher education and scientific research institutions. There are important theoretical and practical significances to explore applicable and effective lateral safety warning methods of heavy vehicles. One of the purposes of this paper is to provide a good theoretical basis for the core technology of heavy vehicle safety features for our country's independent research and development. Aiming at the issue of lateral security of heavy vehicle for road conditions in deteriorative weather, this paper constructs the framework of the lateral security pre-warning system of heavy vehicles based on cooperative vehicle infrastructure. Moreover, it establishes vehicle lateral security statics model through analysis of the force of the car in the slope with section bending and states the parameters of vehicles for no rollover. The side slip is indexed to calculate critical speed of vehicles in a bend. This paper also analyzes the influence of road friction coefficient, the road gradient and the turning radius on the lateral security of the vehicle with critical speed on the asphalt pavement with surface conditions ranging from wet, dry, snowing or icy. The calculation results show that the bad weather road conditions, road friction coefficient and turning radius have obvious influence on the lateral security critical speed. Experimental results indicate that the critical speed error warning is within 4% and it meets the design requirements.

A review on identification methods of road friction coefficient

2021 ◽  
Vol 15 ◽  
Author(s):  
Gengxin Qi ◽  
Xiaobin Fan ◽  
Hao Li
Keyword(s):  
Friction Coefficient ◽  
Autonomous Driving ◽  
Research Direction ◽  
Identification Methods ◽  
Research Status ◽  
The Road ◽  
Road Friction ◽  
Coefficient Measurement ◽  
Estimation System ◽  
Road Friction Coefficient

Background: The development of the tire/road friction coefficient measurement and estimation system has far-reaching significance for the active electronic control safety system of automobiles and is one of the core technologies for autonomous driving in the future. Objective: Estimating the road friction coefficient accurately and in real-time has become the leading research direction. Researchers have used different tools and proposed different algorithms and patents. These methods are widely used to estimate the road friction coefficient or other related parameters. This paper gives a comprehensive description of the research status in the field of road friction coefficient estimation. Method: According to the current research status of Chinese and foreign scholars in the field of road friction coefficient recognition, the recognition methods are mainly divided into two categories: Cause-based and Effect-based. Results: This literature review will discuss the existing two types of identification methods (Cause-based and Effect-based), and the applicable characteristics of each algorithm are analyzed. Conclusion: The two recognition methods are analyzed synthetically, and the development direction of road friction coefficient recognition technology is discussed.

Hierarchical Nonlinear Moving Horizon Estimation of Vehicle Lateral Speed and Road Friction Coefficient

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Chi Jin ◽  
Anson Maitland ◽  
John McPhee
Keyword(s):  
Friction Coefficient ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Global Optimum ◽  
Moving Horizon Estimation ◽  
The Road ◽  
Nominal Model ◽  
Road Friction ◽  
High Level ◽  
Road Friction Coefficient

Abstract In this paper, we address nonlinear moving horizon estimation (NMHE) of vehicle lateral speed, as well as the road friction coefficient, using measured signals from sensors common to modern series-production automobiles. Due to nonlinear vehicle dynamics, a standard nonlinear moving horizon formulation leads to non-convex optimization problems, and numerical optimization algorithms can be trapped in undesirable local minima, leading to incorrect solutions. To address the challenge of non-convex cost functions, we propose an estimator with a two-level hierarchy. At the high level, a grid search combined with numerical optimization aims to find reference estimates that are sufficiently close to the global optimum. The reference estimates are refined at the low level leading to high-precision solutions. Our algorithm ensures that the estimates converge to the true values for the nominal model without the need for accurate initialization. Our design is tested in simulation with both the nominal model as well as a high-fidelity model of Autonomoose, the self-driving car of the University of Waterloo.

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