scholarly journals Path tracking of autonomous vehicle based on adaptive model predictive control

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141988008 ◽  
Author(s):  
Fen Lin ◽  
Yuke Chen ◽  
Youqun Zhao ◽  
Shaobo Wang
Keyword(s):  
Friction Coefficient ◽  
Working Conditions ◽  
Autonomous Vehicle ◽  
Least Square ◽  
Vehicle Speed ◽  
Adaptive Model ◽  
Recursive Least Square ◽  
Tracking Accuracy ◽  
Road Friction ◽  
Road Friction Coefficient

In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimated tire cornering stiffness is used to update vehicle dynamics model and the estimated road friction coefficient is used to update the road adhesion constraint. Moreover, the control parameters consist of prediction horizon, control horizon, and sampling time, all of which are set according to vehicle speed. A co-simulation based on MATLAB/Simulink and CarSim is conducted. The simulation results illustrate that the proposed controller has a great adaptive ability to changing working conditions, especially to speed and road conditions.

Vehicle Trajectory Prediction Based on Road Recognition

2014 ◽  
Vol 599-601 ◽  
pp. 760-766
Author(s):  
Qing Bo Shao ◽  
Hsin Guan ◽  
Xin Jia
Keyword(s):  
Autonomous Vehicle ◽  
Recognition Algorithm ◽  
Least Square ◽  
Prediction Algorithm ◽  
Recursive Least Square ◽  
Trajectory Prediction ◽  
Dynamic Correction ◽  
The Road ◽  
Road Friction ◽  
Vehicle Trajectory

Predicting vehicle trajectory accurately is a crucial task for an autonomous vehicle. It is also necessary for many Advanced Driver Assistance System to predict trajectory of the ego-vehicle’s. In recent years, some vehicles trajectory prediction algorithm is mainly based on a simple Motion Model. This paper puts forward a method which combines road recognition and the hypothesis of steady preview and dynamic correction for trajectory prediction. In the road recognition algorithm, both methods of Kalman Filter (KF) and Recursive Least-Square (RLS) work well to estimate the road slope and road friction coefficient.

Tire–road friction coefficient estimation based on designed braking pressure pulse

2021 ◽  
pp. 095440702098358
Author(s):  
Juqi Hu ◽  
Subhash Rakheja ◽  
Youmin Zhang
Keyword(s):  
Friction Coefficient ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Vehicle Speed ◽  
Two Stage ◽  
Slip Ratio ◽  
Road Friction ◽  
Vehicle Motion ◽  
Road Friction Coefficient

Knowledge of tire–road friction coefficient (TRFC) is valuable for autonomous vehicle control and design of active safety systems. This paper investigates TRFC estimation on the basis of longitudinal vehicle dynamics. A two-stage TRFC estimation scheme is proposed that limits the disturbances to the vehicle motion. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure for reliable estimation of TRFC with minimal interference with the vehicle motion. This stage also provides a qualitative estimate of TRFC. In the second stage, tire normal force and slip ratio are directly calculated from the measured signals, a modified force observer based on the wheel rotational dynamics is developed for estimating the tire braking force. A constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the TRFC for achieving rapid convergence and enhanced estimation accuracy. The effectiveness of the proposed methodology is evaluated through CarSim™-MATLAB/Simulink™ co-simulations considering vehicle motions on high-, medium-, and low-friction roads at different speeds. The results suggest that the proposed two-stage methodology can yield an accurate estimation of the road friction with a relatively lower effect on the vehicle speed.

An Adaptive Nonlinear Differentiable Friction Modeling for Tire-Road Friction Estimation

2015 ◽  
Author(s):  
Zhi-Jun Fu ◽  
Subhash Rakheja ◽  
Wen-Fang Xie ◽  
Xiao-Bin Ning ◽  
Wei-Dong Xie
Keyword(s):  
Parameter Estimation ◽  
Estimation Error ◽  
Friction Model ◽  
Least Square ◽  
Nonlinear Observer ◽  
Accurate Estimation ◽  
Vehicle Speed ◽  
Recursive Least Square ◽  
Friction Modeling ◽  
Road Friction

In this paper, a differentiable friction model is proposed to estimate the longitudinal tire-road friction force of vehicle systems. A novel adaptive nonlinear observer-based parameter estimation scheme has been developed to estimate the parameters of friction model, which requires the signals from the existing sensors signals such as wheel rotational speed and vehicle speed. Different from conventional gradient and recursive least square (RLS) methods, the filtered regression parameter estimation error is introduced in the novel adaptive laws, which can guarantee the observer error convergence to zero and the estimated parameter also convergence to their real value. The Lyapunov method is used to prove the stability of the proposed methods. The robustness of the developing method against bounded disturbances is also proved. Simulation results illustrate that the proposed method can realize relatively accurate estimation of the friction with variations in speed and road gradient.

Simulation Analyzed of Vehicle Operating Safety in Rain Weather Condition Based on ADAMS

2013 ◽  
Vol 361-363 ◽  
pp. 2057-2060
Author(s):  
Hai Lin Si
Keyword(s):  
Friction Coefficient ◽  
Model Simulation ◽  
Lateral Displacement ◽  
Weather Condition ◽  
Coupling Model ◽  
Lane Change ◽  
Vehicle Speed ◽  
Road Condition ◽  
Road Friction ◽  
Road Friction Coefficient

The safety of vehicle operating has tight relation with the road condition; vehicle operating safety accident is easy occurred in badness road condition. This paper studied the vehicle Operating Safety in rain weather condition based on Multi-Rigid Body system Automatic Dynamic Analysis of Mechanical Systems (ADAMS). The models include Vehicle model, Road model, Vehicle and Road coupling model, Simulation module were set up. By changing road friction coefficient, road conditions in sunny day, dry, and rain weather were simulated. Single lane change text and ramp steering text were carried out, and the response output of lateral displacement was obtained. Computation result indicated that in the single lane change when road friction coefficient in rain weather is 0.4, vehicle speed is 60km/h in the single line simulation; the vehicle will be easily out of control. When vehicle speed is 55 km/h, maximum steer value is 70degree in the single lane simulation, vehicle will go haywire. In the ramp steer simulation when vehicle speed is 40 km/h, vehicle will go haywire.

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