Autonomous Vehicle Path Tracking Research Articles

Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm

Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm

Model predictive control for autonomous vehicle path tracking through optimized kinematics

Tracking performance and stability of path tracking is crucial for unmanned vehicles' navigational tasks. Researches on vehicle path tracking controllers primarily rely on dynamic models. In contrast, there are less designs and researches that focus on path tracking controller based on kinematic model. This scarcity may stem from the perceived inadequacy in the accuracy of vehicle kinematic models. This research introduces a novel method for modifying the traditional vehicle kinematic model by incorporating a front wheel steering angle modification function to enhance tracking performance of path tracking controllers based on kinematic models. In terms of control strategy selection, the study opted for nonlinear model predictive control with terminal cost. A controller which is founded on the modified model was used on a simulated vehicle on CarSim-Simulink platform to assess the tracking performance. The simulation was designed with a double-shift line reference trajectory and the initial speeds of the simulated vehicle are 5 m/s and 10 m/s, respectively. The results of the simulations indicate that employing a controller based on the modified model could reduce the peak tracking error by 76.45 % and 43.06 %, and the root mean square of the tracking error was reduced by 73.29 % and 36.06 %, respectively.

An Investigation of Classical Model Predictive Controller Path Tracking Performance of a Two-Wheel and Four-Wheel Steering Vehicle

Studies on self-driving vehicles have become a trend in recent years, and many systems have been developed to enable autonomous manoeuvre. Various methods have been used to improve path-tracking algorithms which increase vehicle performance, including tracking accuracy and stability. Path tracking is one of the primary problems for autonomous vehicles where the vehicle deviates from target paths, which leads to unnecessary counter-correction. Conventional front wheel steering is unable to satisfy the manoeuvre with a high lateral acceleration since the front steering angle is limited in accurately responding to vehicle dynamics. Moreover, the characteristics of front wheel steering vehicles affect handling stability due to the fact that the turning radius is larger than the vehicle itself. This disadvantageous can compromise safety during under-steer and over-steer situations. The main objective of this preliminary study is to investigate the performance of a four-wheel steering system (4WS) in path tracking for autonomous vehicles using a classical model predictive controller (MPC). Conventional two-wheel steering (2WS) tracking performance following the desired driving system with the same MPC controller is compared with 4WS vehicles. The driving system is developed using Driving Scenario Designer to extract the desired yaw angle and lateral position for controller references constructed in Matlab Simulink. Fixed MPC constraint, prediction, control horizon, yaw angle and lateral position weights were used to compare the performance between 2WS and 4WS vehicles. The simulation results show that 4WS is three times better than 2WS vehicles in tracking predetermined paths. 4WS vehicle show 74.55% better performance in lateral position tracking and 68.75% better performance in trailing predetermined yaw angle value. The simulation data from the preliminary study will be used as a guideline to develop an advanced controller of 4WS vehicles.

Disturbance-Observer-Based Barrier Function Adaptive Sliding Mode Control for Path Tracking of Autonomous Agricultural Vehicles With Matched-Mismatched Disturbances

Disturbance-Observer-Based Barrier Function Adaptive Sliding Mode Control for Path Tracking of Autonomous Agricultural Vehicles With Matched-Mismatched Disturbances

Barrier Lyapunov Function-based Backstepping Controller Design for Path Tracking of Autonomous Vehicles

This research proposes a novel BLF-based backstepping controller for path tracking of Autonomous Vehicles (AVs) with unknown dynamics and unmeasurable states. The proposed framework includes: (1) forming geometric-dynamic model of the vehicle by combining the dynamics of the vehicle with the kinematics of the visual measurement system, (2) designing a fixed-time Extended-State Observer (ESO) to estimate the unknown dynamics and unmeasurable states, and (3) introducing a BLF-based controller for faster response and more accurate path tracking compared to previous BLF-based controllers. Besides the novelty of the BLF-based controller, by transforming the closed-loop error dynamics into a unified proportional-derivative (PD)-type structure, an intuitive criterion is proposed to provide a systematic procedure for comparing BLF-based controllers. A combined BLF is further proposed based on this performance criterion to eliminate the sensitivity of BLF-based controllers to the magnitude of the constraint. The stability analysis is performed for the fixed-time ESO and the closed-loop control system. MATLAB/CarSim co-simulation is conducted to evaluate the performance of the proposed control system. The outcomes of the work show that the closed-loop control system is exponentially stable. In addition, it can provide a faster response and result in more accurate path tracking compared to previous BLF-based control systems.

A Novel Robust H∞ Control Approach Based on Vehicle Lateral Dynamics for Practical Path Tracking Applications

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an H∞ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An H∞ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with H∞ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios.

Adaptive model predictive control based on stability index for path tracking of autonomous vehicles considering model error

Autonomous vehicle path tracking control based on model predictive control (MPC) still faces some challenges, such as unknown external environment disturbance and parametric modeling uncertainties. For these problems, this paper puts forward an adaptive model predictive control (AMPC) strategy which combines stability index and model error compensation to improve vehicle path tracking and stability performance. Firstly, the model error identification scheme based on radial basis function neural network (RBFNN) is designed to estimate the uncertainties of vehicle parametric model. Through the identification and compensation of model error, the model error during critical maneuvers can be reduced. Secondly, to improve vehicle maneuverability and yaw stabilization performance, the AMPC controller is realized by adaptive weight adjustment based on MPC. The stability index is designed to schedule the weights of MPC optimization objectives. Finally, the effectiveness and robustness of the path tracking control strategy are verified by CarSim&Simulink co-simulation and hardware-in-the-loop (HIL) test. The results illustrate that the AMPC strategy based on stability index considering model error not only enhances the tracking accuracy, but also improves the handling and stability performance of vehicles.

Ultra-fast tuning of neural network controllers with application in path tracking of autonomous vehicle

Neural network (NN) controllers have shown great potential in solving complex control or decision-making tasks. However, most of the NN controllers either rely on the availability of large datasets or require dense interactions with the environment, which hinders their application in real systems. In this paper, we introduce a model-based reinforcement learning (MBRL) algorithm, aimed at realizing ultra-fast tuning of deep NN controller from a small sample set of real-world data. The algorithm uses Gaussian processes (GPs) to model the unknown dynamics of real system and updates controller parameters through stochastic gradient descent. By using particle-based method for long-term predictions, the algorithm can easily incorporate online state estimators and filters into controller learning, which is conductive to learning from systems with partially measurable states and stochastic control delay. We apply the algorithm to calibrate a deep NN controller for the path tracking of a full-size autonomous vehicle (AV). Simulation and field test results show that the deep NN controller can be well calibrated after only one interaction with the environment and can achieve similar tracking performance to optimization-based methods such as nonlinear model prediction control (NMPC) in various test scenarios by combining with a feed-forward pure pursuit (PP) controller.

Deviation Sequence Neural Network Control for Path Tracking of Autonomous Vehicles

Despite its excellent performance in path tracking control, the model predictive control (MPC) is limited by computational complexity in practical applications. The neural network control (NNC) is another attractive solution by learning the historical driving data to approximate optimal control law, but a concern is that the NNC lacks security guarantees when encountering new scenarios that it has never been trained on. Inspired by the prediction process of MPC, the deviation sequence neural network control (DS-NNC) separates the vehicle dynamic model from the approximation process and rebuilds the input of the neural network (NN). Taking full use of the deviation sequence architecture and the real-time vehicle dynamic model, the DS-NNC is expected to enhance the adaptability and the training efficiency of NN. Finally, the effectiveness of the proposed controller is verified through simulations in Matlab/Simulink. The simulation results indicate that the proposed path tracking NN controller possesses adaptability and learning capabilities, enabling it to generate optimal control variables within a shorter computation time and handle variations in vehicle models and driving scenarios.

The fuzzy system based on vague partitions and its application to path tracking control for autonomous vehicles

As significant carriers of the application of fuzzy set theories, fuzzy systems have been widely used in many fields. However, selecting fuzzifications, fuzzy reasoning engines, and defuzzifications is subjective for Mamdani fuzzy systems, and the fuzzy rule of Takagi-Sugeno-Kang fuzzy systems is less of a linguistic interpretation. Regarding these shortcomings, this paper proposes a fuzzy system based on vague partitions processing information directly from the fuzzy rule base, in which fuzzy rules have explicit semantics. Firstly, the n-dimensional vague partition of the n-dimensional universe is defined based on 1-dimensional vague partitions and the aggregation function, and its properties are discussed. Based on these, we design the new fuzzy system, and investigate its approximation properties which is the theoretical guarantee for applying the fuzzy system. As an application, we combine the fuzzy system with PID control system to deal with autonomous vehicle path tracking control problems. A series of experiments are constructed, and experimental results indicate that the fuzzy system based on vague partitions makes the fuzzy PID control system strong robustness, and has obvious advantages compared with other traditional fuzzy systems for path tracking control problems.

Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in Whale Optimization Algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.

Path Tracking of Autonomous Vehicle Based on NMPC With Pre-Steering

Path Tracking of Autonomous Vehicle Based on NMPC With Pre-Steering

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

Path tracking of varying-velocity 4WS autonomous vehicles under tire force friction ellipse constraints

In this paper, a control scheme is presented to solve the problem of path tracking for varying-velocity autonomous vehicles with higher robust performance while friction force constraints of vehicle tires and ground are considered to avoid appearing the steering lost situation actively in the limit of the tire friction forces by using four wheel steering. To achieve these goals, a new expression of the tire forces and a new controlled system are proposed for the three degree of freedom vehicle model. A longitudinal input-saturation controller is designed to track the desired longitudinal velocity and attenuate the influence of lateral and yaw motions. The virtual control input method is used to design the input-saturation controller and get the desired steering angles to track the desired path for the lateral and yaw motions while a new control problem with desired forces tracking is considered to autonomously avoid the limit of lateral tire friction forces and the steering lost situation appearing. To test these results, lane changing, U-corner turning and straight-line driving for a varying-velocity vehicle are simulated with external disturbances and model uncertainties. Simulation results show that the higher robustness and precise path tracking performances are obtained.

A Novel Enhanced Data-Driven Model-Free Adaptive Control Scheme for Path Tracking of Autonomous Vehicles

A Novel Enhanced Data-Driven Model-Free Adaptive Control Scheme for Path Tracking of Autonomous Vehicles

Finite-Time Autonomous Vehicle Path Tracking Control Based on State Constraint

Finite-Time Autonomous Vehicle Path Tracking Control Based on State Constraint

Linear Quadratic Zonotopic Control of Switched Systems: Application to Autonomous Vehicle Path-Tracking

Linear Quadratic Zonotopic Control of Switched Systems: Application to Autonomous Vehicle Path-Tracking

Comparative Study on Coordinated Control of Path Tracking and Vehicle Stability for Autonomous Vehicles on Low-Friction Roads

This paper presents a comparative study on coordinated control of path tracking and vehicle stability for autonomous vehicles on low-friction roads. Generally, a path-tracking controller designed on high-friction roads cannot provide good performance under low-friction conditions. To cope with the problem, a coordinated control between path tracking and vehicle stability has been proposed to date. In this paper, three types of coordinated controllers are classified according to the controller structure. As an actuator, front-wheel steering, four-wheel steering, and four-wheel independent braking and driving are adopted. A common feature of these types of controllers is that front steering and yaw moment control are adopted as control inputs. To convert the yaw moment control into tire forces generated by combinations of multiple actuators, a control allocation method is applied. For each type, a controller is designed and simulated using vehicle simulation software. From the simulation results, a performance comparison among those controller types is carried out. Through comparison, it is shown that there are small differences among those types of controllers in terms of path tracking.

An improved model predictive control method for path tracking of autonomous vehicle considering longitudinal velocity

In order to increase the accuracy of the path tracking, an improved model predictive control (IMPC) is proposed for autonomous vehicle under road conditions of large curvature, which can enhance the performances of the driving stability and safety. The controller design is implemented in four steps. First, the curvature of road ahead is derived and applied to determine the longitudinal velocity. Thus, the longitudinal velocity is not assumed to be constant, which is the salient feature of the proposed control. Second, the kinematic model of vehicle is established by the Ackermann steering principle. Third, the predictive model is constructed by linearization and discretization of the kinematic model. Fourth, the longitudinal velocity and the front steering angle are imposed on hard constraints, and the constrained objective function is designed and composed of the position deviation and the control increment. Then, we can obtain the optimal results of the longitudinal velocity and the front steering angle. Furthermore, experiment and simulation on the path tracking of an autonomous vehicle are presented. The results show that the proposed control can realize excellent tracking performance under the road conditions of large curvature.

Fixed-time generalized super-twisting control for path tracking of autonomous agricultural vehicles considering wheel slipping

Efficient path tracking control for autonomous agricultural vehicles (AAVs) is the key technology for realizing agriculture 4.0 and precision agriculture, which makes smart farms more sustainable, profitable, and eco-friendly. It is well recognized that model nonlinearity, unknown disturbances, and wheel slipping are common problems in practical AAV systems in operation. To tackle these issues and improve the path tracking performance, a novel fixed-time generalized super-twisting control scheme is proposed for AAVs in this paper. First, a fixed-time integral sliding mode surface is designed for the considered vehicle by fusing the lateral offset and heading offset together. Then, an innovative auxiliary system dynamics is constructed to collaborate with the foregoing sliding mode surface, compensating for the adverse effects of wheel slipping effectively. Subsequently, based on the kinematic offset model of AAVs, a new type of generalized super-twisting controller is designed to achieve superior path tracking with robustness. Unlike existing super-twisting controllers, the generalized super-twisting continuous controller can fully eliminate chattering by replacing the previous discontinuous terms with a nonsmooth term. Further, rigorous theoretical analysis is conducted by raising a fresh Lyapunov function to verify the closed-loop control system stability. Finally, extensive field experiments have been carried out to show that the proposed control scheme significantly outperforms typical comparative control schemes.