Trajectory Tracking Of Autonomous Vehicles Research Articles

PSO based linear parameter varying-model predictive control for trajectory tracking of autonomous vehicles

In this paper, Linear Parameter Varying-Model Predictive Control (LPV-MPC) for trajectory tracking for Autonomous Vehicles (AVs) is proposed. This method is based on the time-varying LPV is the form of the state space representation from the mathematical model of the vehicle. The LPV representation form which uses the dynamic model of the vehicle allows the incorporation of time-varying dynamics, providing a more accurate representation of the vehicle's behavior. The designed LPV-MPC controller for AVs is specifically designed to handle constraints in trajectory tracking. To enhance its performance, Particle Swarm Optimization (PSO) is employed as an optimization technique. PSO is used to tune the weighting matrices of the control parameters, optimizing the system response and improving trajectory tracking performance. To evaluate the effectiveness of the LPV-MPC system, extensive simulations are conducted and results are compared with Linear and Non-Linear MPCs. The main benefit of using the LPV-MPC method is its ability to calculate solutions almost as good as the non-linear MPC version yet significantly reducing the computational cost. The capability of the LPV-MPC controller as compared to the linear version is in its effective tracking, particularly for the non-linear reference trajectories.

Autonomous vehicle tracking control for a curved trajectory

Recently, research about trajectory tracking of autonomous vehicles has significantly contributed to the development of autonomous vehicle technology, particularly with novel control methods. However, tracking a curved trajectory is still a challenge for autonomous vehicles. This research proposes a state feedback linearization with observer feedback to overcome some difficulties arising from such a path. This approach suits a complex nonlinear system such as an autonomous vehicle. This method also has been compared with the linear-quadratic regulator (LQR) method. So, the goal of this research is to improve the control system performance of autonomous vehicles that are stable enough to navigate a curved path. Moreover, the study shows that the developed control law can track the curved path and solve existing problems. However, improvements are still necessary for the vehicle's performance and robustness.

Internal Model-Based Robust Path-Following Control for Autonomous Vehicles

The paper presents a new internal model control (IMC) based control technique for lateral trajectory tracking of autonomous vehicles. The controller’s proposed structure employs a robust, fault-tolerant nonlinear internal servo control with optimal reference generation concerning vehicle yaw stability and physical limitations. The presented inscription of the reference generation creates a convex optimization task that can be used in real-time applications. Improvements in yaw-rate stability of vehicle motion control are first shown through simulation results performed in a Simulink environment. The controller structure was also implemented in a real-time model and was examined in a Mercedes C-Class vehicle. In this article, the simulation results and the real-time measurements are presented. The results show that the proposed controller has high efficiency in disturbance rejection and lower sensitivity towards parameter changes compared to a model predictive control (MPC) structure.

Event-triggered model predictive-preview control strategy for trajectory tracking of autonomous vehicle

This paper proposes an event-triggered model predictive-preview control strategy for autonomous vehicle trajectory tracking. First, the dynamic equation is established based on the preview road curvature and the vehicle’s 2 degree-of-freedom relationship. A model predictive tracking controller is designed by predicting the system dynamics in the future. Second, to reduce the computational burden of the controller, the triggering conditions are designed according to the system’s stability and feasibility. The proposed event-triggered model predictive-preview control strategy not only makes the autonomous vehicles maintain tracking accuracy but also reduces the number of online optimization. Then, asymptotically stable of closed-loop systems are proved by using Lyapunov’s theorem. Finally, simulation experiments show that the proposed strategy has some advantages over traditional model predictive control in terms of improving vehicle tracking accuracy and reducing algorithm complexity.

Deep Reinforcement Learning combined with RRT for trajectory tracking of autonomous vehicles.

Sample inefficiency is a long-standing problem in Deep Reinforcement Learning based algorithms, which shadows the potential of these techniques. So far, the primary approach for tackling this issue is prioritizing the gathered experiences. However, the strategy behind collecting the experiences received less attention, but it is also a legitimate approach for prioritizing. In this paper, the Rapidly exploring Random Trees algorithm and Deep Reinforcement Learning are combined for the trajectory tracking of autonomous vehicles to mitigate the issues regarding sample efficiency. The core of the concept is to utilize the tremendous explorational power of RRT for covering the state space via experiences for the Agent to diversify its training data buffer. The results demonstrate that this approach outperforms the classic trial-and-error-based concept according to several performance indicators.

Investigating the Impact of Magnetorheological Suspension on Trajectory Tracking of Autonomous Vehicles

Investigating the Impact of Magnetorheological Suspension on Trajectory Tracking of Autonomous Vehicles

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC).

In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm. Based on the vehicle dynamics model, the output equation of the autonomous vehicle trajectory tracking control system is constructed, and the auxiliary variable and the dual variable are introduced. The quadratic programming problem transformed from the MPC and the vehicle dynamics constraints are rewritten into the solution of the ADMM form, and a decreasing penalty factor is used during the solution process. The simulation verification is carried out through the joint simulation platform of Simulink and Carsim. The results show that, compared with the active set method (ASM) and the interior point method (IPM), the algorithm proposed in this paper can not only improve the accuracy of trajectory tracking, but also exhibits good real-time performance in different prediction time domains and control time domains. When the prediction time domain increases, the calculation time shows no significant difference. This verifies the effectiveness of the ADMM in improving the real-time performance of MPC.

A Comparison Study of Kinematic and Dynamic Models for Trajectory Tracking of Autonomous Vehicles Using Model Predictive Control

A Comparison Study of Kinematic and Dynamic Models for Trajectory Tracking of Autonomous Vehicles Using Model Predictive Control

Adaptive robust control for reliable trajectory tracking of autonomous vehicle in uncertain driving environment

External disturbances, parameter perturbance, data delay and control lag provoke significant model mismatches. If not properly compensated, they can greatly deteriorate the control performance of autonomous vehicle, such as reduction of tracking accuracy or even loss of stability in extreme. However, existing approaches barely consider these uncertainties together. In the light of this, an adaptive strategy is presented for trajectory tracking control of autonomous vehicle to simultaneously cope with aforementioned factors. First of all, given the dynamic or kinematic characteristics among path, vehicle and steering actuator, an integrated dynamic model is constructed. To handle the control lag of the steering actuator, a first-order model is utilized to approximate the dynamics of the steering subsystem, which is then integrated into the vehicle dynamics to reformulate the tracking model as a lag-free one. Then, the hierarchical robust tracking controller is proposed to acquire reliable control commands. To prevent the system breakdown in the presence of data delay, the delay-dependent criterion is designed via linear parameter varying technique and integral inequality approach. Moreover, the controllers also consider both the H∞ index and the guaranteed cost one to guarantee the effectiveness and robustness of tracking commands. Subsequently, to enhance the adaptability of algorithm, a feedback gains scheduling mechanism is proposed to adaptively tune tracking commands among different robust gains leveraging the phase plane approach. Finally, several comparative cases are conducted in the hardware-in-the-loop platform to verify that proposed strategy has better capability on trajectory tracking in uncertain driving conditions.

Adaptive Asymmetric Time-Varying Integral Barrier Lyapunov Control-Based Trajectory Tracking of Autonomous Vehicles

This paper investigates the lane following and changing maneuvers of autonomous vehicles in the presence of unknown disturbances, taking into account the dynamic system states and input constraints. The integrated longitudinal-lateral and yaw rate dynamics of the vehicle are simultaneously considered to improve the tracking accuracy and system stability when navigating under critical conditions. Then, an adaptive asymmetric time-varying integral barrier Lyapunov control and dynamic surface control scheme are developed to design the active front steering controller, longitudinal controller, and direct yaw moment control controller, which is capable of constraining the system states and control signals within the predefined boundary. In addition, the radius basis function neural network (RBFNN) is employed to estimate the lumped disturbances caused by the parametric uncertainties, external disturbances, and unmodeled dynamics, and the command filter system is used to avoid the explosion of terms phenomenon. Due to the fast and accurate torque response characteristics of the in-wheel motors, the optimization-based method is then implemented to effectively allocate the driving/braking torque to each in-wheel motor so as to improve vehicle performance. The stability of the closed-loop system is comprehensively demonstrated by means of the Lyapunov theory. Finally, the quantitative and qualitative comparisons in different diving scenarios using the Carim-Simulink joint environment are carried out to illustrate the effectiveness and validation of the proposed method.

Coupled lateral and longitudinal trajectory tracking control with a modified vehicle kinematics model for autonomous vehicles

Abstract The paper proposes a coupled lateral and longitudinal trajectory tracking control algorithm based on model predictive control (MPC) to enhance the precision and stability of trajectory tracking of autonomous vehicles. The coupled MPC controller is developed based on a modified vehicle kinematics model, which simultaneously takes the steering angle and the longitudinal speed as control variables. The modified vehicle model and the coupled MPC controller are verified by CarSim and MATLAB/Simulink co-simulation experiments. Simulation results demonstrate that, at the cost of 12 percent increase in average computational time, the proposed coupled MPC controller leads to an approximate 48 percent reduction in tracking error compared to the benchmarking coupled lateral and longitudinal MPC controller based on the classic kinematics model.

A Double-Layer Model Predictive Control Approach for Collision-Free Lane Tracking of On-Road Autonomous Vehicles

This paper proposes a double-layer model predictive control (MPC) algorithm for the integrated path planning and trajectory tracking of autonomous vehicles on roads. The upper module is responsible for generating collision-free lane trajectories, while the lower module is responsible for tracking this trajectory. A simplified vehicle model based on the friction cone is proposed to reduce the computation time for trajectory planning in the upper layer module. To achieve dynamic and accurate collision avoidance, a polygonal distance-based dynamic obstacle avoidance method is proposed. A vertical load calculation method for the tires is introduced to design the anti-rollover constraint in the lower layer module. Numerical simulations, with static and dynamic obstacle scenarios, are conducted on the MATLAB platform and compared with two state-of-the-art MPC algorithms. The results demonstrate that the proposed algorithm outperforms the other two algorithms regarding computation time and collision avoidance efficiency.

Fuzzy predictive Stanley lateral controller with adaptive prediction horizon

The challenge of trajectory tracking of autonomous vehicles (AVs) is a critical aspect that must be effectively addressed. Recent studies are concerned with maintaining the yaw stability to guarantee the customers’ comfort throughout the journey. Most of the geometrical controllers solve this task by dividing it into consecutive point stabilization problems, limiting the controllers’ ability to handle sudden trajectory changes. One research presented a predictive Stanley lateral controller that uses a discrete prediction model to mimic human behavior by anticipating the vehicle’s future states. That controller is limited in its use, as the parameters must be manually tuned for every change in the maneuver or vehicle velocity. This article presents a novel approach for trajectory tracking in autonomous vehicles, by introducing a fuzzy supervisory controller that automatically adapts to changes in the vehicle’s velocity and maneuver by estimating the prediction horizon’s length and providing different weights for each controller. The proposed method overcomes the limitations of traditional controllers that require manual tuning of parameters, making it ready for real-world experiments. This is the main contribution of the research in this paper. The suggested technique demonstrated an advantage over the Basic Stanley controller and the manually tuned predictive Stanley controller in terms of the total lateral error and the model predictive control (MPC) in terms of computational time. The performance is determined by performing various simulations on V-Rep and hardware-in-the-loop (HIL) experiments on an E-CAR golf bus. A broad selection of velocities is used to validate the behavior of the vehicle while working on different maneuvers (double lane change, hook road, S road, and curved road).

Trajectory tracking control considering the transmission backlash of the dual-motor autonomous steering system

In order to solve the tracking error caused by transmission backlash in the dual-motor autonomous steering system, and thus improve the trajectory tracking accuracy of autonomous vehicles, a novel steering control strategy combining sliding model control (SMC) with variable weights and linear quadratic regulator (LQR) is proposed in this paper. Firstly, the vehicle dynamics model is built and the steering system model with contact and backlash modes is established by combining it with the simplified gear backlash system. Secondly, the front wheel angle control based on SMC is designed and the weights of contact mode and backlash mode are optimized by Particle Swarm Optimization (PSO) algorithm. Finally, the results of simulation under typical conditions show that the proposed strategy has better accuracy of angle tracking and stronger adaptability to the change of system load. Furthermore, it is also proved by simulation and Hardware-in-Loop (HiL) test under different trajectory tracking conditions that the LQR controller with proposed strategy has better accuracy of trajectory tracking for autonomous vehicles.

An Integrated Motion Planning Scheme for Safe Autonomous Vehicles in Highly Dynamic Environments

This study proposes a new integrated approach to the motion control of autonomous vehicles, which differs from the conventional method of treating planning and tracking tasks as separate or hierarchical components. By means of the proposed approach we can reduce the side effects on the performance of autonomous vehicles under challenging driving circumstances. To this end, our approach processes both of the aforementioned tasks asynchronously and simultaneously utilizes a multi-threaded architecture to enhance control performance. Meanwhile, the behavior planning feature is integrated into the path-tracking module. Then, a linear parameter-varying model predictive control is deployed for trajectory tracking of autonomous vehicles and compared with the linear model predictive control method. Finally, the control performance of the proposed approach was evaluated through simulation trials on urban roads with placed obstacles. The outcomes revealed that the suggested framework satisfies the processing rate and high-precision criteria, while safely avoiding obstacles, indicating that it is a promising control strategy for real-world applications.

Trajectory Tracking of Autonomous Vehicle: A Differential Flatness Approach With Disturbance-Observer-Based Control

In this article, the problem of trajectory tracking control is studied for autonomous vehicle with consideration of the nonlinearity and coupling characteristics. To achieve accurately trajectory tracking in longitudinal and lateral motions simultaneously, a flatness disturbance-observer-based control scheme is developed to address the kinematic coupling issue of the vehicle, which can avoid extra modeling errors introduced by linearization. The main idea is that, derived from a suitable selection of flat outputs, the complex kinodynamic vehicle model is transformed into three fully-actuated subsystems with same structure, which will provide convenience for controller design. Then, in order to compensate the lumped disturbances caused by modeling and transformation, a disturbance-observer-based control strategy is designed via backstepping. Trajectory tracking control is accomplished through longitudinal, lateral and yaw motions simultaneously. The stability of the closed-loop dynamics is guaranteed in the framework of Lyapunov and input-to-state stability theories. Finally, the CarSim simulation tests demonstrate that the proposed integrated control method can successfully promote the trajectory tracking performance in different manoeuvres.

Research on Path Planning and Trajectory Tracking of Autonomous Vehicle

Research on Path Planning and Trajectory Tracking of Autonomous Vehicle

Trajectory Tracking of Autonomous Vehicle Based on Model Predictive Control With PID Feedback

The simplified vehicle model often results in inaccuracy with respect to the conventional model predictive control (MPC) as it causes steady error in tracking control, which has negative implications for vehicle cornering. This study presents a trajectory planning and tracking framework, which applies artificial potential to obtain target trajectory and MPC with PID feedback to effectively track planned trajectory. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy and steering smoothness compared to that of the conventional MPC control. Especially during negotiating a curve, its steady state error is close to 0.

MPC Based Trajectory Tracking for An Automonous Deep-Sea Tracked Mining Vehicle

Model predictive control (MPC) has been successfully used in trajectory tracking for autonomous vehicles based on certain kinematic model under low external disturbance conditions, but when there are model uncertainties and external disturbances, autonomous vehicles will fail to follow the pre-set trajectory. This paper studies trajectory tracking control based on MPC for an autonomous deep-sea tracked mining vehicle in polymetallic nodule mines with model uncertainty and external disturbances. A MPC algorithm is designed for trajectory tracking. To address model uncertainties caused by vehicle body subsidence and track slippage, a drive wheel speed correction controller is designed by experimental data fitting, and Kalman filtering (KF) and adaptive Kalman filtering (AKF) are introduced to improve tracking performance by rejecting external disturbances especially during curve tracking. To handle dead zones and obstacles during actual operation, an obstacle avoidance strategy is proposed that uses the tri-circular arc obstacle avoidance trajectory with an equal curvature for path re-planning. Finally, Simulink&Recurdyn co-simulations validate the performance of the proposed MPC controller through a comparison with nonlinear MPC(NMPC).

Review article: State-of-the-art trajectory tracking of autonomous vehicles

Abstract. Air pollution, energy consumption, and human safety issues have aroused people's concern around the world. This phenomenon could be significantly alleviated with the development of automatic driving techniques, artificial intelligence, and computer science. Autonomous vehicles can be generally modularized as environment perception, path planning, and trajectory tracking. Trajectory tracking is a fundamental part of autonomous vehicles which controls the autonomous vehicles effectively and stably to track the reference trajectory that is predetermined by the path planning module. In this paper, a review of the state-of-the-art trajectory tracking of autonomous vehicles is presented. Both the trajectory tracking methods and the most commonly used trajectory tracking controllers of autonomous vehicles, besides state-of-art research studies of these controllers, are described.