1403 Protection Function for Active Four-Wheel Steering Vehicle Using Estimation Method for Road Friction Coefficient

2009 ◽  
Vol 2009.84 (0) ◽  
pp. _14-3_
Author(s):  
Osamu Nishihara ◽  
Shintaro Noda ◽  
Masahiko Kurishige
2012 ◽  
Vol 249-250 ◽  
pp. 337-342
Author(s):  
Fen Lin ◽  
Chao Huang

The accurate information of road friction coefficient allows the control algorithm in vehicle activity system to adapt to external driving conditions. This paper developed a slip-slope friction coefficient estimation method based on Unscented Particle Filter. A 7-DOF non-linear vehicle dynamic model was established. The normal force of tire was approximately calculated from the vehicle dynamic model; the slip and longitudinal force of tire were estimated through a combination of tire mechanical model and UPF(Unscented Particle Filter) method. Finally slip-slopes of different adhesion level roads was obtained. Through virtual test environment in ADAMS/Car, the estimation method proposed was verified to be effective and reliable under various road condition. From the method the relationships between the slip-slope and road friction coefficient are achieved.


Author(s):  
Shuo Cheng ◽  
Ming-ming Mei ◽  
Xu Ran ◽  
Liang Li ◽  
Lin Zhao

Knowledge of the tire-road information is not only very crucial in many active safety applications but also significant for self-driving cars. The tire-road information mainly consists of tire-road friction coefficient and road-tire friction forces. However, precise measurement of tire-road friction coefficient and tire forces requires expensive equipment. Therefore, the monitoring of tire-road information utilizing either accurate models or improved estimation algorithms is essential. Considering easy availability and good economy, this paper proposes a novel adaptive unified monitoring system (AUMS) to simultaneously observe the tire-road friction coefficient and tire forces, i.e., vertical, longitudinal, and lateral tire forces. First, the vertical tire forces can be calculated considering vehicle body roll and load transfer. The longitudinal and lateral tire forces are estimated by an adaptive unified sliding mode observer (AUSMO). Then, the road-tire friction coefficient is observed through the designed mode-switch observer (MSO). The designed MSO contains two modes: when the vehicle is under driving or brake, a slip slope method (SSM) is used, and a recursive least-squares (RLS) identification method is utilized in the SSM; when the vehicle is under steering, a comprehensive friction estimation method is adopted. The performance of the proposed AUMS is verified by both the matlab/simulinkCarSim co-simulation and the real car experiment. The results demonstrate the effectiveness of the proposed AUMS to provide accurate monitoring of tire-road information.


2014 ◽  
Vol 8 (1) ◽  
pp. 292-296
Author(s):  
Zhi-Guo Zhao ◽  
Min Chen ◽  
Nan Chen ◽  
Yong-Bing Zhao ◽  
Xin Chen

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.


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