Vehicle Longitudinal Velocity Research Articles

Improved Vehicle Longitudinal Velocity Estimation Using Accelerometer Based Intelligent Tire

ABSTRACT An intelligent tire–based algorithm was developed to reinforce the vehicle longitudinal velocity estimation, from the vehicle inertial measurement unit (IMU). A tire was instrumented using a triaxis accelerometer (intelligent tire) in an instrumented vehicle with an IMU, and a global positioning system (GPS) based speed sensor (VBOX) as the ground truth for vehicle velocity. A testing matrix was developed, including two tire inflation pressures, two normal loads, and variable speed between 4 m/s to 14 m/s. A signal processing algorithm was developed to analyze the data from the accelerometer. Variational mode decomposition and Hilbert spectrum analysis were used for extracting features from each tire revolution. Later, a machine learning algorithm was trained to estimate the velocity using the acceleration data from the intelligent tire. Because the sampling rates of the IMU data and the intelligent tire data were different, sensor fusion was implemented. This calculated velocity was then used to correct the IMU-based estimated velocity. This new velocity can be used to enhance the performance of all advanced chassis control systems, such as anti-lock braking system (ABS) and electronic stability program (ESP).

Bifurcation analysis of static stability of a tractor-semitrailer under circular motions

This paper analyses a general qualitative depiction of changes in the configuration of the non-linear model of a tractor-semitrailer with excessive turnability under stationary circular motions within a broad range of control parameters, such as the longitudinal velocity of motion and angle of rotation of the wheels of the controlled unit. It allows us to foresee the jackknifing effect and to estimate the conditions necessary for safe travel. The proposed analysis combines the transparent geometric approach to analyse the configuration of a tractor-semitrailer, which was developed earlier by J.P. Pauwelussen, and the ability to investigate real bifurcations of the non-linear equation of handling and the non-linear equation of folding angle tractor-semitrailer analytically. Based on this approach, we obtain new analytical dependencies that determine the boundaries of divergent loss of stability of the entire manifold of stationary states of a tractor-semitrailer model and estimates of the maximum values of the angle of folding under different values of the vehicle's longitudinal velocity. This is achieved without the prior determination of the manifold of stationary states itself.

Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network

Vehicle dynamics control systems have a fundamental role in smart and autonomous mobility, where one of the most crucial aspects is the vehicle body velocity estimation. In this paper, the problem of a correct evaluation of the vehicle longitudinal velocity for dynamic control applications is approached using a neural networks technique employing a set of measured samples referring to signals usually available on-board, such as longitudinal and lateral acceleration, steering angle, yaw rate and linear wheel speed. Experiments were run on four professional driving circuits with very different characteristics, and the vehicle longitudinal velocity was estimated with different neural network training policies and validated through comparison with the measurements of the one acquired at the vehicle's center of gravity, provided by an optical Correvit sensor, which serves as the reference (and, therefore, exact) velocity values. The results obtained with the proposed methodology are in good agreement with the reference values in almost all tested conditions, covering both the linear and the nonlinear behavior of the car, proving that artificial neural networks can be efficiently employed onboard, thereby enriching the standard set of control and safety-related electronics.

Gain-scheduling LPV synthesis H∞ robust lateral motion control for path following of autonomous vehicle via coordination of steering and braking

This paper proposes a robust gain-scheduling lateral motion control strategy via coordination of the active front-wheel steering (AFS) and direct yaw moment control (DYC) for path following of autonomous vehicle. Distinguishing from other relevant researches, a seamless application of the DYC is designed to reduce the influence of braking for the vehicle longitudinal dynamics. In order to achieve this objective, the weighting function with a self-scheduling parameter is designed. Based on the above, a novel LPV (Linear Parameter Varying) synthesis H∞ performances lateral motion controller is designed considering the vehicle longitudinal velocity as a time-varying parameter. The path following is realised through achieving a desired yaw rate, which is generated based on the path-following dynamics. The feedback gains can be derived by solving the LMI (Linear Matrix Inequalities). Furthermore, the physical limitations of the actuators (steer-by wire and brake-by-wire) are considered by weighting functions. Finally, experiments in the HIL system are carried out to verify the effectiveness and real-time performance of the proposed control strategy, and the results show that the proposed controller has good tracking capability under different manoeuvres and can reduce the possibility of actuator saturation and the influence of braking for the vehicle longitudinal dynamics.

Improved Vehicle Localization Using On-Board Sensors and Vehicle Lateral Velocity

Vehicle localization is essential for intelligent and autonomous vehicles. To improve the accuracy of vehicle stand-alone localization in highly dynamic driving conditions during GNSS (Global Navigation Satellites Systems) outages, this paper proposes a vehicle localization system based on vehicle chassis sensors considering vehicle lateral velocity. Firstly, a GNSS/On-board sensors fusion localization framework is established, which could estimate vehicle states such as attitude, velocity, and position. Secondly, when the vehicle has a large lateral motion, nonholonomic constraint in the lateral direction loses fidelity. Instead of using nonholonomic constraint, we propose a vehicle dynamics/kinematics fusion lateral velocity estimation algorithm, which combines the advantage of vehicle dynamic model in low dynamic driving conditions and the advantage of kinematic model in highly dynamic driving conditions. Thirdly, vehicle longitudinal velocity estimated by WSS (Wheel Speed Sensor) and lateral velocity estimated by proposed method are as measurements for the localization system. All information is fused by an adaptive Kalman filter. Finally, vehicle experiments in U-turn maneuver and left-turn maneuver at a traffic intersection are conducted to verify the proposed method. Four different methods are compared in the experiments, and the results show that the estimated position accuracy of our method is below half a meter during a 5s GNSS outage and could keep a sub-meter-level during a 20s GNSS outage while the vehicle has a relatively large lateral motion.

Estimation of reliable vehicle dynamic model using IMU/GNSS data fusion for stability controller design

This study deals with the enhancement of reliability of vehicle dynamic model for the stability controller design by estimating the effect of uncertainties in both tire forces and vehicle dynamics. In this respect, any selected initial kinetic model of vehicle is updated at each instant by adding some complementary terms using a novel prediction approach. The proposed estimation method uses the information fusion between the inertial measurement unit (IMU) and the aided systems including the global navigation satellite system (GNSS) and the calibrated compass system. The bias of acceleration sensors and the drift of gyroscope are compensated using a novel adaptive algorithm by which the estimator weights are tuned automatically. Therefore, the vehicle longitudinal and lateral velocities together with the yaw rate are accurately estimated with a high frequency. The proposed estimation algorithm is mathematically analyzed for the stochastic stability and experimentally evaluated through real-world vehicular tests. Accordingly, a nonlinear controller for vehicle directional stability is designed based on the updated dynamic model within the CarSim software environment as a virtual vehicle experiment platform. The obtained results reveal that, the designed data fusion algorithm remarkably improves the estimation performance in the presence of system and measurement uncertainties. Also, the controller designed by the estimated vehicle dynamic model will be reliable and robust in the presence of uncertainties.

Cross-combined UKF for vehicle sideslip angle estimation with a modified Dugoff tire model: design and experimental results

The knowledge of key vehicle states is crucial to guarantee adequate safety levels for modern passenger cars, for which active safety control systems are lifesavers. In this regard, vehicle sideslip angle is a pivotal state for the characterization of lateral vehicle behavior. However, measuring sideslip angle is expensive and unpractical, which has led to many years of research on techniques to estimate it instead. This paper presents a novel method to estimate vehicle sideslip angle, with an innovative combination of a kinematic-based approach and a dynamic-based approach: part of the output of the kinematic-based approach is fed as input to the dynamic-based approach, and vice-versa. The dynamic-based approach exploits an Unscented Kalman Filter (UKF) with a double-track vehicle model and a modified Dugoff tire model, that is simple yet ensures accuracy similar to the well-known Magic Formula. The proposed method is successfully assessed on a large amount of experimental data obtained on different race tracks, and compared with a traditional approach presented in the literature. Results show that the sideslip angle is estimated with an average error of 0.5 deg, and that the implemented cross-combination allows to further improve the estimation of the vehicle longitudinal velocity compared to current state-of-the-art techniques, with interesting perspectives for future onboard implementation.

Popov-H∞ Robust Path-Tracking Control of Autonomous Ground Vehicles with Consideration of Sector Bounded Kinematic Nonlinearity

Abstract This paper proposes a new approach to cope with the kinematic nonlinearity in the H∞ vehicle path-tracking controller synthesis problem. The kinematic nonlinearity presented in the vehicle lateral error state is found to satisfy the sector-bound condition. By isolating the sector bounded nonlinearity via an upper linear fractional transformation (LFT), a Lur'e system is formulated. A nominal robust controller is synthesized to meet both the Popov-H∞ criterion and the regional pole placement requirement. A polytopic gain-scheduling technique is subsequently employed to accommodate the effect of the varying vehicle longitudinal velocity. Finally, an instant-turning maneuver and a sharp lane-changing maneuver are tested in CarSim-Simulink joint simulations whose results demonstrate the superiority of the proposed Popov-H∞ controller over a conventional H∞ controller.

Trajectory Tracking and Integrated Chassis Control for Obstacle Avoidance With Minimum Jerk

Intelligent vehicles are expected to perform emergency lane change maneuvers to avoid a collision. During this aggressive maneuver, a high jerk may occur, which reduces the comfort level and may be harmful to vehicle occupants. The present paper addresses autonomous collision avoidance in the context of minimum jerk. First, the desired trajectory described by the desired path and desired velocity profile is generated using quintic polynomials. These quintic polynomials are derived using the Euler-Lagrange equations for the functional defined as the time integral of the squared resultant jerk. The generation of the trajectory depends on the essential parameters, which are the initial longitudinal vehicle velocity, the desired final lateral position, and the tire-road friction coefficient. As a result of nondimensionalization and algebraic manipulations, the collision avoidance problem reduces to a nondimensionalized equation that is an implicit equation in one unknown, which is the lane change aspect ratio, and an input capturing the essential parameters. The plot of the lane change aspect ratio with respect to the input yields a curve that indicates the last point at which a collision can be avoided. The sliding mode control method is used to translate the trajectory tracking errors into the reference values of the total longitudinal force and the centers of percussion lateral accelerations that are the inputs to the tire force distributor. This distributor allocates these reference values to the tires by reducing the tire force usage. Numerical simulations at different initial longitudinal vehicle velocities demonstrate the effectiveness of the controller in avoiding the obstacle and keeping the vehicle motion stable.

Design, Analysis and Application of Single-Wheel Test Bench for All-Electric Antilock Braking System in Electric Vehicles

An antilock braking system (ABS) is one of the most important components in a road vehicle, which provides active protection during braking, to prevent the wheels from locking-up and achieve handling stability and steerability. The all-electric ABS without any hydraulic components is a potential candidate for electric vehicles. To demonstrate and examine the all-electric ABS algorithms, this article proposes a single-wheel all-electric ABS test bench, which mainly includes the vehicle wheel, the roller, the flywheels, and the electromechanical brake. To simulate dynamic operation of a real vehicle’s wheel, the kinetic energy of the total rotary components in the bench is designed to match the quarter of the one of a commercial car. The vertical force to the wheel is adjustable. The tire-roller contact simulates the real tire-road contact. The roller’s circumferential velocity represents the longitudinal vehicle velocity. The design and analysis of the proposed bench are described in detail. For the developed prototype, the rated clamping force of the electromechanical brake is 11 kN, the maximum vertical force to the wheel reaches 300 kg, and the maximum roller (vehicle) velocity reaches 100 km/h. The measurable bandwidth of the wheel speed is 4 Hz–2 kHz and the motor speed is 2.5 Hz–50 kHz. The measured results including the roller (vehicle) velocity, the wheel velocity, and the wheel slip are satisfactory. This article offers the effective tools to verify all-electric ABS algorithms in a laboratory, hence saving time and cost for the subsequent test on a real road.

Automated vehicle lateral guidance using multi-PID steering control and look-ahead point reference

In this paper, a complete analysis of the influence of a look-ahead distance and the longitudinal vehicle velocity on the vehicle lateral dynamics is made in order to choose an adequate control str...

Fault Tolerant Control of Vehicle Lateral Dynamic Using a New Pneumatic Forces Multiple Model

This paper presents a new accurate multiple model of nonlinear pneumatic lateral forces. The bicycle representation is used in order to build up an easy implemented vehicle dynamic model. Moreover, the Takagi–Sugeno fuzzy approach is applied in order to handle the vehicle model nonlinearities. This structure allows for taking into account the small variation of the vehicle longitudinal velocity. Subsequently, a Fault Tolerant Control strategy that is based on a bank of fuzzy Luenberger observers is proposed. The robustness of the control scheme against external noises is guaranteed by applying H∞ performance. Sufficient stability conditions that are based on Lyapunov method are formulated as Linear Matrix Inequality. Thus, allowing the computation of the observers’ and the controllers’ gains by using MATLAB. Finally, the simulation examples are performed to show the effectiveness of our proposal.

Non‐fragile multi‐objective linear parameter‐varying controller design for vehicle lateral stability

This study investigates the robust non-fragile controller design for vehicle lateral stability, with considering the uncertainties of tire cornering stiffness and actuator saturation. The vehicle longitudinal velocity is assumed to be time varying and the velocity-dependent parameters are described within a trapezoid polytope. The norm-bounded perturbation of controller gain is taken into account which is more practical. By solving the linear matrix inequalities, the non-fragile linear parameter-varying controller is obtained to satisfy the mixed H ∞ and G H 2 performance of the vehicle dynamics system. With the designed controller, the sideslip angle and yaw rate of vehicle are restricted effectively to guarantee the lateral stability and handling performance. Two simulations are carried out to validate the effectiveness of the designed controller.

Velocity-based Lateral Stability Control for Four-wheel Independently Actuated Electric Vehicles with Homogeneous Polynomial Approach

This paper presents a control strategy to enhance the lateral dynamics stability and handling performance of the four-wheel independently actuated (FWIA) electric vehicles (EVs). The vehicle longitudinal velocity uncertainty and controller saturation are considered, a double layers control scheme is adopted. In the upper layer, the homogeneous polynomial parameter-dependent approach is introduced to track the uncertainty problem, and a multi-objective controller is designed to obtain the desired external yaw moment. In the lower layer, an optimal force distribution method with considering the distribution error and tire workload is employed to allocate the desired external yaw moment into forces of the four in-wheel motors. Simulation results verify the effectiveness of the proposed control strategy.

Model-Predictive-Control-Based Path Tracking Controller of Autonomous Vehicle Considering Parametric Uncertainties and Velocity-Varying

The automated steering control technology is crucial for an autonomous vehicle, but due to parametric uncertainties and time varying, the performance of automated steering control can be degraded. Therefore, a vehicle automated steering controller based on a model predictive control (MPC) approach is proposed in this article. First, considering tire nonlinear characteristics, the state and control matrices are modified, then the time-varying vehicle speed is considered and a linear parameter varying lateral model is established through utilizing a polytope with finite vertices to describe vehicle longitudinal velocity. Then, the MPC-based vehicle path tracking controller, which is robust against parameter uncertainties, is designed; the proposed controller can be solved via a set of linear matrix inequalities (LMI), which are derived from Lyapunov asymptotic stability and the minimization of the worst case infinite horizon quadratic objective function. The proposed control system is evaluated by both cosimulations of MATLAB/Simulink & CarSim and real-bus tests. Results show the effectiveness of the proposed controller, and it can ensure the control accuracy and strong robustness.

Velocity-based robust fault tolerant automatic steering control of autonomous ground vehicles via adaptive event triggered network communication

This paper proposes a velocity-based robust fault tolerant automatic steering control approach for autonomous ground vehicles via an adaptive event triggered network communication mechanism. Firstly, as the vehicle longitudinal velocity is not fixed but time-varying, a polytope with finite vertices is adopted to construct a linear parameter varying vehicle system model. Secondly, since network-induced delay and actuator fault inevitably occur in the control system, a novel Lyapunov function is formulated to guarantee the asymptotical stability of the closed-loop system. Moreover, an advanced event triggered communication scheme is proposed for the co-design of robust automatic steering controller, which is quite effective to reduce network communication burden and enhance resource utilization of the shared band-limited in-vehicle communication network. Finally, numerical simulations on two cases are carried out to evaluate and verify the effectiveness and robustness of the proposed controller. Compared with existing studies, the proposed controller contributes greatly to the economization of communication resource and improvement of path following performance and lateral stability.

Longitudinal and Lateral Velocity Estimation based on Covariance Inverse Robust Fusion

Accurate estimation of vehicle longitudinal and lateral velocity is meaningful for internal system. Aiming at improving the accuracy of velocity estimation, the Covariance inverse (CI) based fusion method is applied. A vehicle dynamics model is established and the CI method is analysed. Finally, a joint simulation of CarSim and MATLAB/Simulink is conducted and the results show the advantages of proposed method.

Robust LMI-Based H-Infinite Controller Integrating AFS and DYC of Autonomous Vehicles With Parametric Uncertainties

Autonomous vehicles’ dynamics stability control is one key issue to ensure safety of self-driving. However, vehicle uncertainties and time-varying parameters could weaken the performance of autonomous vehicle stability control. Therefore, this article proposes a novel robust linear matrix inequality (LMI)-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> -infinite feedback algorithm for vehicle dynamics stability control, and this algorithm controls vehicle steering system and brake system via direct yaw moment control (DYC) and active front steering control (AFS). The presented controller is robust against vehicle parametric uncertainties, including the vehicle mass and vehicle longitudinal velocity. A linear parameter varying lateral model is constructed utilizing polytopic uncertainty method considering time-varying vehicle longitudinal velocity and mass, where a polytope that contains finite vertices is established to contain all of the possible selections for uncertainty parameters. Then, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> -infinite feedback controller integrating DYC and AFS is derived via LMI technique. Finally, experimental results based on hardware-in-the-loop (HIL) platform illustrate that the presented controller has better performance of ensuring autonomous vehicle dynamics stability than other controllers.

Simulation of vehicle’s lateral dynamics using nonlinear model with real inputs

Basic understanding of vehicle’s lateral dynamics may be achieved by using relatively simple mathematical-mechanical models. However, these models are often linearized and do not take into account nonlinear phenomena in vehicle’s lateral dynamics and highly nonstationary nature of vehicle’s vibrations in real driving conditions. One form of nonlinear nonstationary model of vehicle’s lateral dynamics is developed in this paper. Simulations were conducted using experimentally recorded values of the steering wheel angle for generation of the front wheels steer angle, and vehicle longitudinal velocity as inputs to the model. Results of simulations of vehicle angular vibrations around the vertical axis, angular body side slip vibrations, tire slip angles, vehicle lateral acceleration wheel forces and inertial force are presented and discussed. Finally, conclusions regarding possible further use of the developed model were drown.

Improved Adaptive Path Following Control System for Autonomous Vehicle in Different Velocities

Path following is the basic technology of the autonomous vehicle (AV), many preview control methods have been widely applied to path following tasks. However, less of them take the variable vehicle velocities into account. In fact, the velocity is an important factor affecting the tracking accuracy. Especially, when an AV is in high velocity, it is not easy to achieve path following with high accuracy. To improve the adaptivity of path following in different velocities, an improved adaptive path following control system (PFCS) constructed by a course angle optimal referential model (CAORM) and a model predictive controller (MPC) is developed in this paper. The CAORM can provide the referential course angle, according to the vehicle longitudinal and lateral velocities, which significantly improves the adaptivity of the proposed PFCS in different velocities. And the CAORM is mainly implemented by a fuzzy inference system and a novel preview model, using human driving experience. The MPC is applied to realize the course control with high accuracy via manipulating the steering angle. Finally, the tracking performance of the PFCS is verified via simulation experiments on the Simulink-CarSim platform.