Lateral Control Of Autonomous Vehicles Research Articles

Multi‐variable and multi‐objective gain‐scheduled control based on Youla‐Kucera parameterization: Application to autonomous vehicles

AbstractThis paper presents a Youla‐Kucera based interpolation between a set of Linear Parameter‐Varying (LPV) controllers, each one being a gain‐scheduled of Linear Time‐Invariant (LTI) controllers designed separately for different operating points. The gain‐scheduling is achieved based on Youla‐Kucera (YK) parameterization. A generalized LPV‐YK control structure is designed to interpolate between various LPV controllers. The closed‐loop system is proved to guarantee the quadratic stability for any continuous/discontinuous interpolating signals in terms of a set of Linear Matrix Inequalities (LMIs). The proposed method can help multi‐variable and multi‐objective systems to achieve high performances at different operating conditions and different critical situations regardless of the interpolation rate. A numerical example is simulated to show the importance of the proposed method to achieve different objectives for lateral control of autonomous vehicles. In addition, the approach has been tested on a real Renault ZOE vehicle to validate its real performance, and compare it with a standard polytopic LPV controller.

Methodology to Obtain the State Feedback Gains for Lateral Control of the Autonomous Vehicles

This paper introduces a methodology for deriving state feedback gains aimed at enhancing the lateral control of Autonomous Vehicles (AV). Autonomous Vehicles represent a groundbreaking technological advancement falling under the category of intelligent transportation systems. In developed nations, AVs are regarded as a potential solution to mitigate injuries resulting from road accidents. The AV system encompasses three distinct modules: Perception, Reference Generation, and Control. Among these, the Control module plays a pivotal role in ensuring the autonomy of the vehicle. Within the realm of existing literature, various lateral control systems have been documented. However, those relying on state feedback mechanisms have failed to provide a well-defined and systematic methodology for acquiring the requisite control gains. Furthermore, these approaches have not addressed the issue of varying vehicle speeds in the context of gain determination. Hence, the primary objective of this article is to present a comprehensive methodology for the systematic derivation of appropriate control gains for the AV lateral control system. The proposed methodology commences by positioning the system’s poles in accordance with the desired eigenvalues. Subsequently, the system is subjected to different sets of poles. Finally, an in-depth analysis of input and output data is conducted, with the aim of identifying the poles that most effectively minimize system errors.

Development of a Finite Element Model to Analyze the Lateral Drift of Autonomous Vehicles and Its Impacts on Rutting Depth on Flexible Pavements Under Mixed Traffic Scenarios

The continuous development of technologies, including intelligent networking, machine vision, and new energy vehicles, has made autonomous driving vehicles a research focus. Autonomous vehicles significantly affect road performance under mixed traffic flow conditions with their excellent lateral control ability and driving stability. A finite element model is established to analyze the impact of these modes on pavement deformation under maximum air temperature in Nanjing, China. Based on the simulation results, it has been shown that lateral control of autonomous vehicles significantly reduces pavement deformation. The proportion of autonomous vehicles affects the difference in distribution modes, and the uniform distribution mode is the most favorable for horizontal distribution. Additionally, the edge lane experiences less pavement deformation than the center lane and varies more when the distribution pattern changes. By comparing the deformation of the center and edge lanes, the edge lane experiences a significant reduction in deformation under the uniform distribution mode, with a maximum reduction of 73.7%. Comparing the pavement deformation of center and edge lanes under normal distribution mode and uniform distribution mode shows that safe solutions for the lateral distribution of autonomous vehicles depend on the proportion of autonomous vehicles. The optimal driving speed for autonomous vehicles is 80 km/h under the uniform distribution model, while under the normal distribution model it is 90 km/h or 110 km/h. These findings provide a theoretical basis for the lateral control of autonomous vehicles in the future and propose safe solutions for the lateral distribution of autonomous vehicles.

Adaptive reference aware MPC for lateral control of autonomous vehicles

This work addresses the design of a path tracking controller for autonomous vehicles. It reformulates the Reference Aware MPC in order to guarantee closed-loop stability, while maintaining a safe and comfortable ride, and minimizing wear and tear of vehicle components. Stability is proved via Lyapunov techniques. Furthermore, to adapt the response of the controller online while in operation, a novel model for the nonlinear curvature response of the vehicle is proposed. This model is estimated online by means of Kalman filtering. Both the proposed controller and curvature response model are evaluated with simulations and through experiments on a Scania construction truck, where the advantages to the previous state-of-the-art are highlighted and discussed.

Lateral control for autonomous wheeled vehicles: A technical review

AbstractAutonomous driving has the ability to reshape mobility and transportation by reducing road accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and more productivity as significant driving time will be gained and used in other activities instead. Autonomous vehicles are complex systems consisting of several modules that perform perception, decision‐making, planning, and control. Control is essential for achieving automatic driving; it is basically divided into longitudinal control that handles speed tracking and lateral control which ensures accurate steering. The latter is primordial in path tracking applications and recent research has witnessed a huge leap in this field. The aim of this paper is to provide a technical survey of the latest research on the lateral control of autonomous vehicles as well as to highlight technical challenges and limits for further developments.

A Path Following Lateral Control Scheme for Four-Wheel Independent Drive Autonomous Vehicle Using Sliding Mode Prediction Control

Four-wheel independent drive autonomous vehicle (4WIDAV) is constantly influenced by uncertainties, including parameter fluctuation, modeling error, and external disturbance during the path following process. If those uncertainties are not handled properly, 4WIDAV may deviate from the desired path or even become unstable. Motivated by this issue, a path following lateral control scheme based on modified sliding mode prediction control (SMPC) is proposed for 4WIDAV. First, an SMPC framework is built, which introduces the prediction process into the approaching process of sliding mode dynamic trajectory to alleviate the chattering phenomenon. Second, a lumped uncertainty term containing the modeling error, parameter fluctuation, and external disturbance is introduced in the prediction process of the SMPC framework. The accurate estimation of lumped uncertainty aforementioned is realized through an extended state observer (ESO). Third, the torque distribution method based on tire force observation is designed to ensure the driving stability of the 4WIDAV. Finally, typical driving maneuvers are performed in simulations and experiments to verify the effectiveness of the proposed scheme. Compared with the existing methods, the proposed control scheme can improve the tracking performance up to 69.4% and 52.8% in simulation and experiment, respectively. Therefore, the proposed scheme can be used as a theoretical reference for path following lateral control of autonomous vehicle.

LPV-Based Autonomous Vehicle Lateral Controllers: A Comparative Analysis

This paper presents the design and experimental validation of grid-based and Linear Fractional Transformation (LFT) approaches for the lateral control of autonomous vehicles. These new methodological approaches are compared together with the classical polytopic approach from the theoretical design to the real implementation on a real automated Renault ZOE vehicle. A solution is proposed to deal with both lane change and lane tracking problems, using a single LPV controller, by adapting the look-ahead distance. Each LPV controller is designed based on LPV/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> concept. Performance comparison includes computational costs, vehicle performance (i.e. lateral tracking error or control effort optimization) and on-board integration complexity. Simulation and experimental results on a private test track are included to support main findings.

Research on lateral control of autonomous vehicle based on driver steering model

Path tracking is an important issue in autonomous driving research. In this paper, based on the vehicle two-degree-of-freedom linear dynamics model for path tracking, the study of vehicle path tracking is carried out by applying model predictive control. A linear state space model of the vehicle is established based on the linear two-degree-of-freedom dynamics model, which provides the basis for the path tracking research. Based on the established state space model and the model predictive control method, the path tracking control algorithm is established using the preview point of driver steering model instead of the prediction part in the traditional model predictive control algorithm, which reduces the complexity of the algorithm and greatly improves the real-time performance. Finally, the effectiveness of the algorithm is verified by the joint software simulation of CarSim and Simulink.

State-of-the-Art Review on Recent Advancements on Lateral Control of Autonomous Vehicles

The most well-known research on driverless vehicles at the moment is connected autonomous vehicles (CAVs), which reflects the future path for the self driving field. The development of connected autonomous vehicles (CAVs) is not only increasing logistics operations, but it is also opening up new possibilities for the industry’s sustainable growth. In this review, we will explore the cloud-controlled wireless network-based model of cyber physical aspect of the autonomous vehicle, which is coupled with unmanned aerial vehicles (UAVs). Additionally, this model is Internet of Things (IoT) managed and AI-based, with a blockchain-based security mechanism. Additionally, we’ll focus on lateral control in autonomous driving, particularly the lane change maneuver, taking social behavior into account. Here, we briefly reviewed Vehicle-to-Everything (V2X) communication, which is carried out by on-board sensors and connected wireless medium that enhance the lane departure processes while retaining human driver behavior relying on obstacle avoidance.

Gain‐scheduled steering control for autonomous vehicles

This study presents a linear parameter varying (LPV) approach for the lateral control of autonomous vehicles, in order to take into account the whole operating domain of longitudinal speeds, as well as the variation of the look-ahead distance. Combining a dynamical vehicle model with look-ahead dynamics, together with an identified actuator model including an input delay, the closed-loop performances can be achieved and the tracking capabilities can be improved for every speed. This is obtained in particular through ad hoc representation of the look-ahead time as a parameter-dependent function. An H ∞ LPV control problem is formulated considering parameter-dependent weighting functions, allowing the control adaptation for all speeds. The synthesis is performed using the gridding approach, in order to account for varying parameter rate. The proposed steering control system has been implemented on a real electric Renault Zoe car. The performances are therefore assessed experimentally on a real test track with a varying longitudinal speed profile, and compared with a classical LPV polytopic controller, which proves the advanced lane-tracking capabilities of the proposed methodology.

Robust Nonlinear Control Using Barrier Lyapunov Function Under Lateral Offset Error Constraint for Lateral Control of Autonomous Vehicles

In this study, we propose a robust nonlinear control method using the barrier Lyapunov function (BLF) under the lateral offset error constraint for lateral control of autonomous vehicles. Furthermore, for the application of the BLF-based control method to lateral control, we propose a second-order lateral dynamics scheme based on the look-ahead distance of the vehicle. In the second-order lateral dynamics, the system functions with unknown parameters, and the external disturbances can be lumped into a disturbance term. The proposed method consists of an extended state observer (ESO) and a nonlinear controller. The ESO is designed to estimate the full state variable and disturbance, including the system modeling and external disturbance. The nonlinear controller is developed using the BLF to compensate for the disturbances and to guarantee the constraint for the lateral offset error. Consequently, the proposed method satisfies the output constraint in the presence of disturbances and modeling uncertainties as well as improves the lateral control performance using only the lateral offset error at the look-ahead distance. The lateral control performance of the proposed method is validated using CarSim and MATLAB/Simulink.

Curvature-Based Geometric Approach for the Lateral Control of Autonomous Cars

Several approaches exist for the lateral control of autonomous vehicles. Among them are the geometric approaches. They are shown to be robust to disturbances and able to manage complex tracks. Their main advantage lies on the fact that they are explainable, in the sense that their behavior can be analyzed to provide guarantees about their limitations. However, they do not give the quality of results that can be obtained using other control principles, mostly because of design issues. This paper aims to tackle these issues by proposing a novel geometric approach based on Frenet Serret formulas to reach the level of quality proposed by the other approaches, while still benefiting from the advantages of geometric approaches. A numerical analysis of the proposed control approach show its advantage: Simulation results and tests on a real autonomous car are provided.

Longitudinal and lateral control of autonomous vehicles in multi‐vehicle driving environments

Lane changes in multi-vehicle driving environments are one of the most challenging manoeuvres for autonomous vehicles. The key innovation of this study is to develop an integrated longitudinal and lateral trajectory planning and tracking control algorithm under vehicle-to-vehicle communication. This algorithm includes two levels: trajectory planning and path-following control. In the upper level, considering riding comfort, a collision-free lane-changing trajectory cluster is generated under different lane change durations. Then, the most appropriate trajectory from this cluster is provided by selecting the optimal lane change duration considering vehicle dynamics safety, collision avoidance of surrounding vehicles and driver preference. At the bottom level, a multiple-input multiple-output triple-step non-linear approach is proposed in the longitudinal and lateral path-following controller design. The stability of the closed-loop system is rigorously proven based on the Lyapunov function. Finally, the effectiveness of the proposed algorithm is verified with a high-fidelity and full-car model on the veDYNA platform.

Hierarchical Lateral Control Scheme for Autonomous Vehicle with Uneven Time Delays Induced by Vision Sensors.

Vision-based sensors are widely used in lateral control of autonomous vehicles, but the large computational cost of the visual algorithms often induces uneven time delays. In this paper, a hierarchical vision-based lateral control scheme is proposed, where the upper controller is designed by robust H∞-based linear quadratic regulator (LQR) algorithm to compensate sensor-induced delays, and the lower controller is based on logic threshold method, in order to achieve strong convergence of the steering angle. Firstly, the vehicle lateral model is built, and the nonlinear uncertainties induced by time delays are linearized with Taylor expansion. Secondly, the state space of the system is augmented to describe such uncertainties with polytopic inclusions, which is controlled by an H∞-based LQR controller with a low cost of online computation. Then, a lower controller is designed for the control of the steering motor. According to the results of the vehicle experiment as well as the hardware-in-the-loop (HIL) experiment, the proposed control scheme shows good performance in vehicle’s lateral control task, and exhibits better robustness compared with a conventional LQR controller. The proposed control scheme provides a feasible solution for the lateral control of autonomous driving.

Lateral Control of an Autonomous Vehicle

The asymptotic stabilization problem for a class of nonlinear under-actuated systems is studied and solved. Its solution, together with the back-stepping and the forwarding control design methods, is exploited in the control of the nonlinear lateral dynamics of a vehicle. Even though the theoretical studies of the lateral control of autonomous vehicles are traditionally applied to lane keeping cases, the results can be applied to broader range of areas, such as lane changing cases. The comparison between the performances of the closed-loop systems with the given controller and a typical human driver is given and demonstrates the speediness and the effectiveness of the feedback controller.

Lateral control of autonomous vehicles based on learning driver behavior via cloud model

In order to achieve the lateral control of the intelligent vehicle, use the bi-cognitive model based on cloud model and cloud reasoning, solve the decision problem of the qualitative and quantitative of the lateral control of the intelligent vehicle. Obtaining a number of experiment data by driving a vehicle, classify the data according to the concept of data and fix the input and output variables of the cloud controller, design the control rules of the cloud controller of intelligent vehicle, and clouded and fix the parameter of cloud controller: expectation, entropy and hyper entropy. In order to verify the effectiveness of the cloud controller, joint simulation platform based on Matlab/Simulink/CarSim is established. Experimental analysis shows that: driver's lateral controller based on cloud model is able to achieve tracking of the desired angle, and achieve good control effect, it also verifies that a series of mental activities such as feeling, cognition, calculation, decision and so on are fuzzy and uncertain.

Lateral control of autonomous vehicles based on fuzzy logic

Autonomous vehicles have attracted considerable attention in the research community and industry. This paper addresses a problem in designing lateral control law and develops a strategy to determine the given speed of autonomous vehicles. An improved method for calculating the lateral offset and heading angle error is proposed to reduce the impact of reference path data noise. Multiple fuzzy inference engines are used to design the steering controller and determine the given driving speed, including the forward and backward directions. The stability condition is given to guide the design of fuzzy inference engines. Satisfactory simulation and experimental results have been obtained from different reference paths.

A novel fused neural network controller for lateral control of autonomous vehicles

We present a novel fused feed-forward neural network controller inspired by the notion of task decomposition principle. The controller is structurally simple and can be applied to a class of control systems that their control requires manipulation of two input variables. The benchmark problem of inverted pendulum is such example that its control requires availability of the angle as well as the displacement. We demonstrate that the lateral control of autonomous vehicles belongs to this class of systems and successfully apply the proposed controller to this problem. The parameters of the controller are encoded into real value chromosomes for genetic algorithm (GA) optimization. The neural network controller contains three neurons and six connection weights implying a small search space implying faster optimization time due to few controller parameters. The controller is also tested on two benchmark control problems of inverted pendulum and the ball-and-beam system. In particular, we apply the controller to lateral control of a prototype semi-autonomous vehicle. Simulation results suggest a good performance for all the tested systems. To demonstrate the robustness of the controller, we conduct Monte-Carlo evaluations when the system is subjected to random parameter uncertainty. Finally experimental studies on the lateral control of a prototype autonomous vehicle with different speed of operation are included. The simulation and experimental studies suggest the feasibility of this controller for numerous applications.

Lateral Control of Autonomous Vehicles Using Multi-Model and Fuzzy Approaches

This paper discusses the fundamental issues in vehicle lateral motion control within Automated Highway Systems. Lane keeping control and lane changing control are concerned. One major challenge for lateral controller design is the smoothness and robustness under different vehicle conditions. A multi-model fuzzy controller, which includes four local controllers, is then proposed for both the operations of lane keeping and lane changing. These four local controllers are set up for four vehicle speed regions respectively. A fusion block is then introduced to ensure smooth and accurate transition between the different local controllers. The proposed controller inherits the advantages of both the multi-model control and fuzzy control. Simulations show that it could get good performances in the whole range of operation speed, and could also repel the system uncertainties such as changes in vehicle load, movement inertia and wheel cornering stiffness. Furthermore, the calculation procedure of the proposed controller is not complex, and is rather rapid. It appears a promising control algorithm for realtime embedded applications.

A Study on Lateral Control of Autonomous Vehicles via Fired Fuzzy Rules Chromosome Encoding Scheme

In this paper, we propose a novel Fired Rules Chromosomes (FRC) encoding scheme for a fuzzy controller tuned by Genetic Algorithms (GA). The proposed method improves the optimization speed through the reduction of the search space. In addition, an improvement in convergence is demonstrated. The fuzzy controller optimized by the FRC scheme is employed to maintain the lateral position of an autonomous vehicle. The robustness of the controller to parameter variation is studied by Monte-Carlo analysis. Simulation and experimental studies demonstrate the performance of the lateral controller.