Preview Follower Theory Research Articles

Driver–Automated Cooperation Driving Authority Optimization Framework for Shared Steering Control

In this paper, we introduce the preview-follower theory for modeling the trajectory-tracking controller of an automated system using model predictive control (MPC). The primary contribution of this research lies in enhancing tracking accuracy and driving safety when the driver and automated system share similar driving intentions, while also enabling a rapid transfer of driving authority to the human driver in cases of differing intentions. To verify the effectiveness of the proposed driving authority optimization framework, both simulation and driver-in-the-loop experiments were conducted under conditions of consistent and inconsistent driving intentions between the human driver and the autonomous driving system. The results of both experiments demonstrated that the proposed shared steering cooperative control and driving authority optimization framework not only significantly improves vehicle tracking accuracy but also promptly aligns with the driver’s intentions.

Risk Field Model of Driving and Its Application in Modeling Car-Following Behavior

Microscopic modeling of driving behavior is the basis for traffic design and traffic simulation studies and can be applied to automated driving systems to provide human-like decision making. Previous modeling methods can be mainly divided into scenario-based modeling methods and field theory-based modeling methods. Scenario-based models are based on behavior theories that can explain behavioral mechanisms and field theory-based models are convenient for application to different scenarios. Combining two behavior theories and field theory, this paper aims to present a novel method to uniformly model the driving behavior in different scenarios. Risk homeostasis theory and preview-follower theory are used as the theoretical foundation, and field theory is utilized to connect the two behavior theories. A new risk field model is constructed for better coupling these behavior theories. Integrating these theories, this study then develops a subjectively perceived risk quantification method and a trajectory and motion planning model, which are validated using naturalistic data in car-following scenarios. Results show the effectiveness of this method and this model with reference to an effective risk quantification index (safety margin) and in comparison with the classical models (desired safety margin model and intelligent driver model) using naturalistic data in car-following scenarios.

Modeling and simulation of approaching behaviors to signalized intersections based on risk quantification

The stop/go decisions made by drivers who are approaching signalized intersections during yellow period will affect the safety and efficiency of intersections. Existing research mostly modeled drivers’ decision-making behaviors using real-world driving data, while these datasets were collected in different traffic flows and road environments, and it is difficult to develop models suitable for different intersections. Aiming at explaining the approaching behaviors to signalized intersections from the perspective of human behavioral mechanism, this study establishes a driving behavior model framework, including a risk field model of dynamic traffic control elements independent on yellow duration, and a trajectory planning model constructed according to the risk homeostasis theory and preview-follower theory. Probabilities of passing the stop line during yellow period and the distribution of acceleration and deceleration rates when passing are obtained in the simulation by the Monte Carlo method. Results show the validity of the proposed model and its applicability to drivers with different desired risks. Compared to the proposed model, drivers are more inclined to use smaller acceleration rates or greater deceleration rates when entering intersections in observed cases. The intervention of reaction time may decrease the probabilities of passing. This study is an indispensable supplement to our previous study, contributing a unified model based on risk quantification to comprehensively describe the risk of the traffic environment, and is an attempt to promote the development of driving behavior models.

Design and experimental evaluation of an efficient MPC-based lateral motion controller considering path preview for autonomous vehicles

Lateral motion control, a core autonomous driving technology, still faces the significant challenge of accurately tracking the reference path under complex and changeable driving maneuvers. In this regard, this study develops an efficient model predictive control (MPC)-based lateral motion controller considering path preview to improve the robustness and computational efficiency in large lateral acceleration or high-speed lateral motion control. As a typical model-based approach, the accuracy of the MPC predictive model substantially affects the controller’s robustness. Thus, to improve the robustness of the lateral motion controller, a tire parameter online adaptive module (TPOAM) is proposed to update the MPC predictive model online to reduce the model mismatch. Unlike other online adaptation methods, the proposed TPOAM does not rely on complex and multi-parameter tire models or look-up tables. Through delay augmentation, the proposed method accurately models the steering system delay and compensates it in the MPC predictive model. In the engineering implementation of the MPC-based lateral motion controller, the long prediction horizon substantially deteriorates the computational efficiency of the receding optimization. Addressing this issue, the preview-follower theory is introduced into the predictive model to make full use of the future path information. The tracking deviation at the corresponding preview point can be obtained by previewing maneuvers similarly to human drivers at each time in the prediction horizon. This tracking deviation is also considered part of the MPC cost function. The total prediction horizon of the lateral motion controller considering path preview can be effectively extended while almost no computing time increment. The designed controller is verified on an autonomous vehicle platform in the low-speed large curvature and high-speed lane changing scenarios. Experimental results show that the proposed controller can effectively improve the robustness and computational efficiency of lateral motion control compared with the typically used MPC-based lateral motion approach.

A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles

Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.

Adaptive trajectory tracking control strategy of intelligent vehicle

The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.

Path tracking control for inverse problem of vehicle handling dynamics

A path tracking controller based on active disturbance rejection control(ADRC) theory is presented in this paper to solve path tracking problem in inverse vehicle handling dynamics. The basic idea behind the work is to design an active disturbance rejection controller according to yaw rate and lateral displacement during a vehicle travels along a prescribed path to generate an expected trajectory which guarantees minimum clearance to the prescribed path. Aiming at this purpose, using preview follower theory, a linear extended state observer based on lateral displacement is designed. Considering yaw angle of vehicle, a non-linear combination function combined error of lateral displacement as well as error of yaw angle is designed according to monotone bounded hyperbolic of tangent function. Finally, a real vehicle test is executed to verify the rationality of the path tracking controller. At the same time, according to characteristics of pavement file in Carsim, a 3-D virtual pavement model is established and ride comfort simulation of random pavement is carried out in the software model. The results show that the minimum lateral position error of the generated path tracking trajectory can be good indicators of successful solving of the path tracking problem in inverse vehicle handling dynamics for ADRC. More precisely, there is higher calculation accuracy for the algorithm of the ADRC to solve the path tracking problem. The study can help drivers easily identify safe lane-keeping trajectories and area.

窄道自动驾驶系统初探

在狭窄路况下，部分驾驶者因驾驶技术差会出现难于安全通过的问题，因此本论文提出窄道自动驾驶设计构想。论文首先介绍适用于窄道自动驾驶的车载环境感知系统，重点针对三种窄道情况提出路径规划方案，并结合横向控制策略中的预瞄跟随理论和纵向控制策略中的多模块组合思想，从理论上初探了窄道自动驾驶系统。 In narrow roads, as part of drivers for poor driving technology will be difficult to drive through the road safely, therefore this paper puts forward the narrow channels autopilot design vision. Paper first introduces the self-driving car environment perception system for narrow channels, focusing on three kinds of schemes for narrow channels, and combines with preview-follower theory included in lateral control strategy and the combined module which is in the longitudinal control strategy. This paper introduces the narrow channel autopilot system in theory preliminarily.

A Fuzzy Path Preview Algorithm for the Rice Transplanting Robot Navigation System

Path preview is the key to realizing automatic navigation control that can guide the navigational direction of the vehicle trace. The preview point must be regulated dynamically according to the predetermined navigational features of the actual path preview control to obtain the ideal path trace result. Based on the “preview follower theory” combined with the driver’s experience and utilizing the Matlab (The MathWorks, Inc., Natick, Massachusetts, USA) fuzzy control toolbox, a fuzzy algorithm of the preview point has been proposed in the paper. The navigation control system embedded in the path planning and preview control algorithm has been applied in the experiment to trace a predetermined navigational straight way, 90°curve and arc way to operate a rice transplanting robot. The results showed that the lateral DRMS (distance root mean square error) of the straight trace is approximately 0.33 m, and the maximum error is approximately 0.71 m; the lateral DRMS of the 90°curve trace is approximately 0.73 m, and the maximum error is approximately 1.85 m; the lateral DRMS of the arc way trace is approximately 1.03 m, and the maximum error is approximately 2.56 m; and the total mean error is approximately 0.6 m. According to the outcome of the experiment, the relevant algorithm can basically meet the requirements of rice transplanting robot guidance.

Closed-Loop Handling Stability of 4WS Vehicle with Yaw Rate Control

To improve the vehicle handling stability, a four-wheel steering (4WS) controller with yaw rate tracking is proposed in this paper. A driver/ vehicle closed-loop system is built based on the preview-follower theory to evaluate the system’s manoeuvring performance. Next, the system driving on a double lane-change road and on a low adhesion road are simulated and compared with conventional front-wheel steering vehicle. The Fourier transform method is developed to analyse the dynamics of the closed-loop system in the frequency domain. The results show that the proposed 4WS vehicle has the preferred response characteristics of sideslip angle and yaw rate, and is robust regarding tire cornering stiffness, which guarantees the handling stability in emergency braking condition.

Intelligent Control for Mobile Robot Path Tracking Based on Advanced PSO

The four-wheeled mobile robot driving by rear-wheel has some characteristics such as nonholomic constraint, complex model and nonlinear and time variability of steering system. An intelligent PID path tracking method based on preview follower theory was proposed, which acquired by improved particle swarm optimization algorithm, and the self-tuning of control of system was achieved. The result indicated that the mobile robot controlled by the proposed approach can follow various reference paths accurately and robustly. Compares to conventional PSO control method ,possesses the advantages of high precise ,great adaptability and robustness.

Stability Sliding Mode Control for Dual-Motor Independent Drive Electric Vehicle Based on Transient Reference Model

Motors of Independent Drive Electric Vehicle can be controlled independently, which can enrich the method of stability control for vehicle. In order to fully take use of the advantage，The man-vehicle system with eight degrees of freedom ,which includes Magic Formula tire model and driver model based on Preview Follower Theory, was set up by using MATLAB/SIMULINK. Considering the complexity of the driving condition and the nonlinear time-varying characteristics of the vehicle system, a Sliding mode controller based on transient reference model was set up, which selected Yaw rate as Control variable and two motors as actuators. The simulation results show that handling and stability of vehicle can be improved greatly by the Sliding mode controller.

Stability Control for Dual-Motor Independent Drive Electric Vehicle Based on Sliding Mode Control

Two motors of Dual-Motor Independent Drive Electric Vehicle can be controlled independently, which can enrich the method of stability control for vehicle. In order to fully take use of the advantage，The man-vehicle system with eight degrees of freedom ,which includes Magic Formula tire model and driver model based on Preview Follower Theory, was set up by using MATLAB/SIMULINK. Considering the complexity of the driving condition and the nonlinear time-varying characteristics of the vehicle systems, a controller based on Sliding mode variable structure control theory was set up, which selected Yaw rate as Control variable and two motors as actuators. The simulation results show that handling and stability of vehicle can be improved greatly by the Sliding mode controller.

A Driver Modeling Based on the Preview-Follower Theory and the Jerky Dynamics

Based on the preview optimal simple artificial neural network driver model (POSANN), a new driver model, considering jerky dynamics and the tracing error between the real track and the planned path, is established. In this paper, the modeling for the driver-vehicle system is firstly described, and the relationship between weighting coefficients of driver model and system parameters is examined through test data. Secondly, the corresponding road test results are presented in order to verify the vehicle model and obtain the information on drive model and vehicle parameters. Finally, the simulations are carried out via CarSim. Simulation results indicate that the jerky dynamics need to be considered and the proposed new driver model can achieve a better path-following performance compared with the POSANN driver model.

Understanding and Modeling the Human Driver Behavior Based on MPC

Better understanding driver behavior is essential for autonomous vehicles, as well as simulation, evaluation and optimization of a driver-vehicle closed-loop system. In this paper, the human driver behavior is first discussed in the form of perception, decision, and execution. In the following, the modelling of the driver behavior is proposed based on the preview-follower theory. The driver model includes three parts which are perception module, decision making module and execution module. The decision making module contains longitudinal control using proportional-integral-derivative (PID) controller and lateral control using model predictive control (MPC) method. Moreover, the response of muscle and nerve is modelled in the form of execution module. Typical simulations are carried out using vehicle dynamics software veDYNA and indicate that the proposed driver model is suitable for representative path following at varying vehicle speed.

基于神经网络分数阶PI<sup>a</sup>D<sup> β</sup>控制的智能车研究<br>The Intelligent Vehicle Research on Neural Network Fractional Order Control

本文将BP神经网络与分数阶有机结合，采用预瞄跟随理论对高度非线性的智能车进行简化建模，设计了基于神经网络分数阶控制的智能车。最后对设计进行了仿真验证，并与普通分数阶及常规PID的控制效果进行了比较分析，仿真验证表明了神经网络分数阶控制器在动态性能、稳态误差等方面都要优于一般分数阶及常规PID控制，证明了本设计的有效性。 In this paper, we combined the BP neural network with the fractional order organically. Using preview follower theory simplified modeling for highly nonlinear intelligent vehicles, designed of fractional-order control based on neural network intelligent vehicles. Finally, designed with simulation and validation, and with normal fractional order and conventional PID control effect for a comparative analysis, simulation showed that the fractional-order controller based on neural networks in terms of dynamic performance, steady-state and error are better than the general fractional order and conventional PID control, proved the effectiveness of the design.

Evaluation of a vehicle directional control with a fractional order PDμ controller

In this paper, a novel controller, the fractional order PDμ controller, is designed to improve the performance of the driver-vehicle system. First, fractional calculus and fractional order PIλDμ controller are introduced. A control algorithm for vehicle directional control using the fractional order PDμ controller is then presented. Based on preview-follower theory, the on-line tuning method of the fractional order PDμ controller is designed. By comparing simulated and experimental results, the validity and robustness of the proposed fractional order PDμ controller in the closed loop system are verified. Finally, comprehensive evaluations are performed between the systems with the fractional order PDμ controller and with an integer PD controller. The results demonstrate that the use of the fractional order controller leads to an improvement of the performance of the driver-vehicle system.

An analytical driver model for arbitrary path following at varying vehicle speed

In this paper, an analytical driver model in the form of a simple artificial neural network is presented. Based on the preview-follower theory and error elimination algorithm, all the driver model parameters can be calculated analytically. In order to extend the driver model's application scope, all the driver parameters are fitted as the functions of vehicle speed, and a preview point searching algorithm is proposed to enable the driver model to follow an arbitrary designated path defined in ground coordinate. Driver/vehicle closed-loop simulations indicate that the proposed driver model is suitable for arbitrary path following at varying vehicle speed.