Autonomous Vehicle Path Tracking Research Articles

Switched model predictive controller for path tracking of autonomous vehicle considering rollover stability

Lateral stability and rollover stability are two most crucial concerns for path tracking of an autonomous vehicle under handling limit conditions. To improve path tracking accuracy and ensure vehicle stability under handling limit conditions, this paper proposes a force-driven switched MPC path tracking control strategy that coordinates active front-wheel steering with an external yaw moment. Firstly, a linear time-varying tire model is modelled using the affine approximate linearisation method to describe rear tire dymamics. And vehicle dynamics is modelled to predict vehicle states in the prediction horizon. Then, a force-driven switched MPC control strategy is proposed based on the system prediction model. The vehicle lateral stability and rollover stability are restricted using maximum tire lateral force and the zero-point moment method, respectively. Finally, according to the simulation results under normal speed and limiting speed conditions, a force-driven switched MPC control strategy can improve control accuracy and ensure vehicle stability.

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to the multiple actuators. The proposed algorithm has two modules: reference state decision and MPC-based vehicle motion controller. Reference state decision module determines reference state profiles consisting of yaw rate and velocity in order to overcome the limitation of the error dynamics-based path tracking controller, which requires several assumptions on the reference path. The MPC-based vehicle motion controller is designed with a linear time-varying vehicle model in order to optimally allocate the control effort to each actuator. A linear time-varying MPC is adopted to reduce computational burden caused by using a non-linear one. The effectiveness of the proposed algorithm is validated via simulation on MATLAB/Simulink and CarSim. The simulation results show that the proposed algorithm improves the reference tracking performance by effectively distributing the control effort to the steering angle and driving force of each actuator.

Observer-based finite frequency [formula omitted] state-feedback control for autonomous ground vehicles

This paper studies the problem of path tracking of Autonomous Ground Vehicles (AGVs) in the presence of sideslip angles. An observer is designed to estimate both the sideslip angle and the vehicle yaw rate, based on which an observer-based controller is established such that the closed-loop system is stable and the vehicle follows a desired path accurately. In particular, the nonlinear vehicle dynamics model is reformulated as a Linear Parameter Varying (LPV) system, and a finite frequency H∞ criteria is satisfied such that the disturbances are attenuated effectively, the parameter-dependent gain matrices are calculated simultaneously by solving a convex optimization problem. Simulation results show the effectiveness of the method proposed.

Path tracking based on model predictive control with variable predictive horizon

Model predictive control is one of the main methods used in path tracking for autonomous vehicles. To improve the path tracking performance of the vehicle, a path tracking method based on model predictive control with variable predictive horizon is proposed in this paper. Based on the designed model predictive controller for path tracking, the response analysis of path tracking control system under the different predictive horizons is carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. Then, taking the lateral offset, the steering frequency and the real-time of the control algorithm as comprehensive performance indexes, the particle swarm optimization algorithm is designed to realize the adaptive optimization for the predictive horizon. The effectiveness of the proposed method is evaluated via numerical simulation based on Simulink/CarSim and hardware-in-the-loop experiment on an autonomous driving simulator. The obtained results show that the optimized predictive horizon can adapt to the different driving environment, and the proposed path tracking method has good comprehensive performance in terms of path tracking accuracy of the vehicle, driving comfort and real-time.

Autonomous vehicle path tracking control considering the stability under lane change

Considering that when a vehicle travels on a low friction coefficient road with high speed, the path tracking ability declines. To keep the performance of path tracking and improve the stabilization under that situation, this article presents approaches to estimate the parameters and control the vehicle. First, the key states of the vehicle and the road adhesion coefficient are estimated by the unscented Kalman filter. This is followed by applying the linear time-varying model-based predictive controller to achieve path tracking control, and the initial tire steering angle control rate is obtained. Finally, the steering angle compensation controller is simultaneously designed by a simple receding horizon corrector algorithm to improve vehicle stability when the path is tracked on a low-adhesion coefficient or at high speed. The performance of the proposed approach is evaluated by software CarSim and MATLAB/Simulink. Simulation results show that an improvement in the performance of path tracking and stabilization can be achieved by the integrated controller under the variable road adhesion coefficient condition and high speed with 110 km/h.

An adaptive modified neural lateral-longitudinal control system for path following of autonomous vehicles

The full-fledged development and the practical use of autonomous vehicles (AVs) would be a great technological achievement and would substantially reduce the enormous damages caused by driving accidents to life and property. Technology based companies such as Google and Audi are getting closer to realizing the dream of seeing fully AVs on the road. A vehicle’s severely nonlinear dynamics due to the forces acting between road and vehicle tires, the coupling characteristic, and the uncertainties of parameters such as wheel moment of inertia and vehicle mass have made it rather difficult to approximate a precise mathematical model of vehicle dynamics. In this paper, to overcome these challenges we propose a model-independent control method based on improved adaptive neural controllers for path tracking control of AVs. In the structure of these improved neural controllers, we employ interval type-2 fuzzy sets (IT2FS) as activation functions. Despite the interdependence of a vehicle’s longitudinal and lateral motions, many of the research works on the path tracking of AVs have only focused on lateral motion control. By using the inputs of steering angle and torque, the presented control scheme tackles the simultaneous control of lateral and longitudinal moves. Results obtained from the lateral controller based on an improved neural network (NN) have been analyzed first at a constant velocity of 20 m/s and with/without considering parametric uncertainties. Then the longitudinal controller based on the improved NN is compared with sliding mode and common NN based controllers. Finally, the results obtained by simulating the simultaneous control of lateral and longitudinal motions indicate maximum tracking errors of 0.04 m (for lateral path following) and 0.02 m/s (for longitudinal velocity) and demonstrate the desirable performance of the proposed control approach.

Design and development of sensor based Automatic steering control system for automobiles

Drowsy driving is the major problem seen throughout the world which usually happens when the driver has not slept enough, and this state of mind may lead to accidents. Above being the case, the project helps driver to keep him alert by giving an alarm and to minimize accidents due to driver error, distractions and drowsiness. The research derives, implements, tunes and compares selected path tracking methods for controlling a car-like robot along a predetermined path. The model mainly aims on using active steering control to increase safety and to reduce both accidents and drivers work load. The objective of the project is to design and develop a steering control system for the path tracking of autonomous vehicle & to understand the stability of the vehicles on curved road. The steering controller controls the steering actuator to follow the desired steering angle. A servo motor is installed to control the steering handle, and it can transmit the steering force using a belt and pulley. The developed mechanism is a steering controller that is applied to a micro controller. However, because of a dead band, the path tracking performance and stability of autonomous vehicles are reduced. To overcome this, steering system and programme is developed. As a result of the steering system and programme, the performance and stability are improved. GPS shows the geographical coordinates which specifies any given location on the earth surface as latitude and longitude. Ultrasonic sensor used for detecting the obstacles. A magnetometer compass shows the quadrants. Arduino UNO board is used like a mother board in the project. Finally, the project augments the literature with a comprehensive collection of important path tracking ideas, a guide to their implementations and, most importantly, an independent and realistic comparison of the performance of these various approaches.

Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

Model Predictive Controller (MPC) is a capable technique for designing Path Tracking Controller (PTC) of Autonomous Vehicles (AVs). The performance of MPC can be significantly enhanced by adopting a high-fidelity and accurate vehicle model. This model should be capable of capturing the full dynamics of the vehicle, including nonlinearities and uncertainties, without imposing a high computational cost for MPC. A data-driven approach realised by learning vehicle dynamics using vehicle operation data can offer a promising solution by providing a suitable trade-off between accurate state predictions and the computational cost for MPC. This work proposes a framework for designing an MPC with a Neural Network (NN)-based learned dynamic model of the vehicle using the plethora of data available from modern vehicle systems. The objective is to integrate an NN-based model with higher accuracy than the conventional vehicle models for the required prediction horizon into MPC for improved tracking performances. The proposed NN-based model is highly capable of approximating latent system states, which are difficult to estimate, and provides more accurate predictions in the presence of parametric uncertainties. The results in various road conditions show that the proposed approach outperforms the MPCs with conventional vehicle models.

Implementation of MPC-Based Path Tracking for Autonomous Vehicles Considering Three Vehicle Dynamics Models with Different Fidelities

Model predictive control (MPC) algorithm is established based on a mathematical model of a plant to forecast the system behavior and optimize the current control move, thus producing the best future performance. Hence, models are core to every form of MPC. An MPC-based controller for path tracking is implemented using a lower-fidelity vehicle model to control a higher-fidelity vehicle model. The vehicle models include a bicycle model, an 8-DOF model, and a 14-DOF model, and the reference paths include a straight line and a circle. In the MPC-based controller, the model is linearized and discretized for state prediction; the tracking is conducted to obtain the heading angle and the lateral position of the vehicle center of mass in inertial coordinates. The output responses are discussed and compared between the developed vehicle dynamics models and the CarSim model with three different steering input signals. The simulation results exhibit good path-tracking performance of the proposed MPC-based controller for different complexity vehicle models, and the controller with high-fidelity model performs better than that with low-fidelity model during trajectory tracking.

MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel

In order to track the desired path as fast as possible, a novel autonomous vehicle path tracking based on model predictive control (MPC) and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits, in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit. Moreover, this scheme was further extended along one moving prediction window. In the MPC controller, the prediction model was an 8-degree-of-freedom (DOF) vehicle model, while the plant was a 14-DOF vehicle model. For lateral control, a sequence of optimal wheel steering angles was generated from the MPC controller; for longitudinal control, the total wheel torque was generated from the PID speed controller embedded in the MPC framework. The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory. The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller. Additionally, the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.

Robust Set-Invariance Based Fuzzy Output Tracking Control for Vehicle Autonomous Driving Under Uncertain Lateral Forces and Steering Constraints

This paper is concerned with a new control method for path tracking of autonomous ground vehicles. We exploit the fuzzy model-based control framework to deal with the time-varying feature of the vehicle speed and the highly uncertain behaviors of the tire-road forces involved in the nonlinear vehicle dynamics. To avoid using costly vehicle sensors for feedback control while favoring the simplest control structure for real-time implementation, a new fuzzy static output feedback (SOF) scheme is proposed. In particular, though the robust set-invariance property and Lyapunov-based arguments, the physical constraints on the steering input saturation and the vehicle state can be taken into account in the control design to improve the driving safety and comfort. The theoretical development relies on the use of fuzzy Lyapunov functions and the non-parallel distributed compensation control concept to reduce the design conservatism. Exploiting some specific convexification techniques, the control design is reformulated as an optimization problem under linear matrix inequalities with a single line search, which are efficiently solved via semidefinite programming techniques. The proposed fuzzy path tracking controller is evaluated through various dynamic driving tests conducted with high-fidelity CarSim/Matlab co-simulations. Moreover, to emphasize the advantages of the new fuzzy SOF controller, a performance comparison with the CarSim driver model is also performed.

Research on autonomous vehicle path tracking control considering roll stability

For autonomous vehicle path tracking control, the general path tracking controllers usually only consider vehicle dynamics’ constraints, without taking vehicle stability evaluation index into account. In this paper, a linear three-degree-of-freedom vehicle dynamics model is used as a predictive model. A comprehensive control method combining Model Predictive Control and Fuzzy proportional–integral–derivative control is proposed. Model Predictive Control is used to control the vehicle yaw stability and track the target path by considering the front wheel angle, sideslip angle, tire slip angles, and yaw rate during the path tracking. Fuzzy proportional–integral–derivative algorithm is adopted to maintain the vehicle roll stability by controlling the braking force of each tire. Co-simulation with CarSim and MATLAB/Simulink shows the designed controller has good tracking performance. The controller is smooth and effective and ensures handling stability in tracking the target path.

An LPV Approach to Autonomous Vehicle Path Tracking in the Presence of Steering Actuation Nonlinearities

This article deals with trajectory tracking for autonomous cars during evasive maneuvers and in the presence of steering actuator nonlinearities. This article develops an LPV MISO H-infinity controller based on the feedback of the lateral error at the center of gravity and the look-ahead distance. The controller architecture offers a way to cope with the effect of the steering nonlinearities, by scheduling one of the control weighting functions. A detailed experimental validation on three different maneuvers (straight driving, wide bend, and a double-lane change) shows the effectiveness of the proposed LPV solution.

Driverless simulation of path tracking based on PID control

In order to realize the path tracking of autonomous vehicles, this paper first simplifies the complex structure of the vehicle into a two-degree-of-freedom kinematic and dynamic vehicle model. based on PID control, the vehicle path tracking controller is designed to calculate the lateral deviation between the expected path and the actual path, and outputs the front wheel rotation angle through the controller, so that the autonomous vehicle can travel well according to the expected path. The control system model is built in the MATLAB/Simulink, and the vehicle path tracking control system is simulated and verified jointly with the CarSim software platform. Simulation results show that the designed control strategy can ensure the path tracking performance of intelligent vehicles at different speeds, and has good tracking accuracy, real-time performance and vehicle driving stability.

Path-tracking and lateral stabilisation for autonomous vehicles by using the steering angle envelope

ABSTRACT In the design of a path-tracking controller for autonomous vehicles, mediating the conflicting objectives among path-tracking, vehicle stabilisation, and low computational cost is a challenging issue. Accordingly, this paper proposes a model predictive control (MPC)-based path-following controller with steering angle envelopes. In contrast to previous MPC-based structure, in which the constraints in terms of the road sides and lateral stabilisation directly impose on the algorithm, in the proposed approach, these aspects are formulated as the steering angle envelopes. This feature enables the controller to simultaneously ensure the tracking performance and computational feasibility. Moreover, considering the trade-off between path-tracking and vehicle stabilisation, the full-length prediction horizon pertaining to the steering angle envelopes are divided into two different parts: the safe road envelope is applied in the short-term, and the stable handling envelope is applied in other part. By modelling vehicles driving under different road conditions with various driving speeds and road adhesion values, the developed controller is compared with several existing control schemes via simulations and hardware-in-the-loop (HIL) experiments. The comparison results demonstrate the effectiveness of the proposed controller in realising path-tracking, lateral stabilisation, and real-time computational capabilities in the best possible manner.

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This article introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this article provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling.

Path tracking controller design for autonomous vehicle based on robust tube MPC

A robust tube MPC controller based on tube-division with a linear time-varying (LTV) model is proposed for autonomous vehicle path tracking. To reduce the conservativeness, a novel offline method is designed to calculate the tubes by dividing the original N-steps invariant sets into sequences of tighter candidate tubes. The propagation limits of the vehicle time-varying parameters within a prediction horizon are used in the division to ensure each candidate tube contains any state trajectory starting at its origin. A corresponding tube will be selected instead of being calculated online at each sampling instant in terms of vehicle states, which makes a more efficient online computation. The results of the simulation show the improved performance of the proposed robust tube MPC controller.

A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

Autonomous vehicle path tracking accuracy faces challenges in being accomplished due to the assumption that the longitudinal speed is constant in the prediction horizon in a model predictive control (MPC) control frame. A model predictive control path tracking controller with longitudinal speed compensation in the prediction horizon is proposed in this paper, which reduces the lateral deviation, course deviation, and maintains vehicle stability. The vehicle model, tire model, and path tracking model are described and linearized using the small angle approximation method and an equivalent cornering stiffness method. The mechanism of action of longitudinal speed changed with state vector variation, and the stability of the path tracking closed-loop control system in the prediction horizon is analyzed in this paper. Then the longitudinal speed compensation strategy is proposed to reduce tracking error. The controller designed was tested through simulation on the CarSim-Simulink platform, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

Path tracking of autonomous vehicle based on adaptive model predictive control

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.