A Vehicle Trajectory Tracking Method With a Time-Varying Model Based on the Model Predictive Control

Su, Xiaohua; Shao, Yiming; Qu, Zhihua

doi:10.1109/access.2019.2963291

Cited by 26 publications

(9 citation statements)

References 15 publications

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“…Some models will also consider the physical and geometric conditions of the road (such as road-friction coefficient and road-line type) and use the vehicle-dynamics model to predict the motion state of the vehicle in the next few seconds. Song et al [1] and Houenou et al [2] presented a trajectory prediction method based on an acceleration-motion model. Sandberg et al [3] and Raigoza et al [4] very recently proposed a novel physics-based trajectory predictor.…”

Section: Literature Reviewmentioning

confidence: 99%

Vehicle-Trajectory Prediction Method for an Extra-Long Tunnel Based on Section Traffic Data

et al. 2023

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The driving situation is complicated in an extra-long tunnel. If a traffic collision happens, car evacuation and people rescue in the tunnel will be more challenging. The driving safety and traffic efficiency of an extra-long tunnel will be considerably enhanced if vehicle trajectory can be precisely assessed and the vehicle driving risk can be predicted in advance. However, due to the limitations of the data capture of vehicle-mounted equipment in tunnels, estimating vehicle trajectory with vehicle-side data is difficult. In this research, a vehicle-trajectory prediction model based on section traffic data in an extra-long tunnel is developed using existing roadside traffic-gathering equipment. The model re-optimizes the driver-sensitivity coefficient by calibrating the parameters of the vehicle-following model based on mining the motion laws of cars under different scenarios. The model is validated using observed velocity and position data, and the average accuracy of velocity prediction is 94.14% and 95.45% for trajectory prediction. The results show that using existing roadside collecting equipment, this model can accurately forecast vehicle trajectories in extra-long tunnels.

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Section: Literature Reviewmentioning

confidence: 99%

Vehicle-Trajectory Prediction Method for an Extra-Long Tunnel Based on Section Traffic Data

et al. 2023

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“…MPC algorithm also has many applications in low-speed complex driving conditions [52]. Considering that the longitudinal speed, road curvature, and adhesion coefficient changes under low-speed complex driving conditions have a large impact on the tracking accuracy and stability of unmanned vehicles, the literature [53][54][55] proposed an optimized MPC controller to improve the tracking performance and stability by considering the influence of road constraints, but the study had the problem of being unable to track the reference path with large curvature changes. Therefore, the literature [56,57] proposed the use of Nonlinear Model Predictive Control (NMPC) control method, and the simulation results showed that the tracking performance and stability of the NMPC controller were substantially improved under low-speed large curvature conditions.…”

Section: Pid Algorithmmentioning

confidence: 99%

A Review of Autonomous Vehicle Path Tracking Algorithm Research

CHEN

Zheng

2022

Preprint

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Driverless technology aims to improve driving safety, accuracy and comfort. Path tracking is a basic component of the motion control module of autonomous vehicles, and its control algorithm directly affects the path tracking effect. Based on the preliminary results of the application of path tracking control algorithm, this paper analyzes the principles, advantages and disadvantages, applications and current research progress of the path tracking algorithm under different working conditions from the perspective of different working conditions at low speed and high speed, and provides an outlook on the future development, aiming to provide reference for future in-depth research.

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“…As mentioned above, the accurate trajectory tracking control in AVs requires both the steering wheel control for regulating the lateral position and the throttle/braking control for adjusting its longitudinal dynamics. A vehicle trajectory tracking method with a time-varying model based on the model predictive control using the vehicle kinematic model was proposed in [21]. Random network delay was introduced in [22] to present the uncertainty of the trajectory tracking model of the AV, which significantly deteriorates the stability of the control system and accuracy of the trajectory tracking.…”

Section: Background and Motivationmentioning

confidence: 99%

State-feedback Switching Linear Parameter Varying Control for Vehicle Path Following Under Uncertainty and External Disturbances

Shen

Chen

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. Most PFCs did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, and norm-bounded external disturbances, the system states and control matrices are modified. In addition, the vehicle's time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. Compared to adaptive MPC, nonlinear MPC, and cascade LPV control, the proposed robust LPV MPC shows improved tracing accuracy along vehicle lateral dynamics. Finally, the state feedback switched LPV control theory with separate Lyapunov functions under both arbitrary switching and average-dwell-time (ADT) switching conditions are studied and applied to cover the path following control in full speed range. Numerical examples, tracking effectiveness, and convergence analysis are provided to demonstrate and ensure the control effectiveness and strong robustness of the proposed algorithms.

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A Vehicle Trajectory Tracking Method With a Time-Varying Model Based on the Model Predictive Control

Cited by 26 publications

References 15 publications

Vehicle-Trajectory Prediction Method for an Extra-Long Tunnel Based on Section Traffic Data

Vehicle-Trajectory Prediction Method for an Extra-Long Tunnel Based on Section Traffic Data

A Review of Autonomous Vehicle Path Tracking Algorithm Research

State-feedback Switching Linear Parameter Varying Control for Vehicle Path Following Under Uncertainty and External Disturbances

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