Vehicle Lateral Velocity Research Articles

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles.

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle’s cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data.

Robust [formula omitted] output-feedback control for path following of autonomous ground vehicles

This paper presents a robust H∞ output-feedback control strategy for the path following of autonomous ground vehicles (AGVs). Considering the vehicle lateral velocity is usually hard to measure with low cost sensor, a robust H∞ static output-feedback controller based on the mixed genetic algorithms (GA)/linear matrix inequality (LMI) approach is proposed to realize the path following without the information of the lateral velocity. The proposed controller is robust to the parametric uncertainties and external disturbances, with the parameters including the tire cornering stiffness, vehicle longitudinal velocity, yaw rate and road curvature. Simulation results based on CarSim–Simulink joint platform using a high-fidelity and full-car model have verified the effectiveness of the proposed control approach.

Vehicle state estimation for anti-lock control with nonlinear observer

Vehicle state estimation during anti-lock braking is considered. A novel nonlinear observer based on a vehicle dynamics model and a simplified Pacejka tire model is introduced in order to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically anti-lock braking control. The approach differs from previous work on vehicle state estimation in two main respects. The first is the introduction of a switched nonlinear observer in order to deal with the fact that in some driving situations the information provided by the sensor is not sufficient to carry out state estimation (i.e., not all states are observable). This is shown through an observability analysis. The second contribution is the introduction of tire-road friction estimation depending on vehicle longitudinal motion. Stability properties of the observer are analyzed using a Lyapunov function based method. Practical applicability of the proposed nonlinear observer is shown by means of experimental results.

Haptic Shared Control in Steering Operation Based on Cooperative Status Between a Driver and a Driver Assistance System

Haptic shared control is expected to achieve a smooth collaboration between humans and automated systems, because haptics facilitate mutual communication. A methodology for sharing a given task is important to achieve effective shared control. Therefore, the appropriate cooperative relationship between a human operator and automated system should be considered. This paper proposes a methodology to evaluate the cooperative status between the operator and the automated system in the haptic shared control of a steering operation using a pseudo-power pair of torque from each agent and the vehicle lateral velocity as each agent's contribution to vehicle motion. This method allows us to estimate cooperative status based on two axes: the initiative holder and the intent consistency between the two agents. A control method for a lane-keeping assist system (LKAS) that enables drivers to change lanes smoothly is proposed based on the estimated cooperative status. A gain-tuning control method based on the estimated cooperative status is proposed to decrease the assistance system's pseudo-power when intent inconsistency occurs. A method for switching the followed lane to match the driver's and assistance system's intentions is also proposed. A user study using a driving simulator is conducted to demonstrate the effectiveness of the proposed methods. The results demonstrate that the proposed methods facilitate smooth driver-initiated lane changes without significantly affecting the driver's torque or steering wheel angle while significantly improve lane-keeping performance.

Robust Weighted Gain-Scheduling $H_{\infty }$ Vehicle Lateral Motion Control With Considerations of Steering System Backlash-Type Hysteresis

A robust weighted gain-scheduling H ∞ control design for ground vehicles possessing steer-by-wire and drive/brake-by-wire functions is presented in this paper. The main control objective is to track vehicle yaw rate reference as well as to regulate vehicle lateral velocity with the hand-wheel steering angle and the external yaw moment as two control efforts. Two major challenges are overcome: 1) the backlash-type hysteresis embedded in the steering system introduces an additional disturbance term to the system and 2) the vehicle longitudinal velocity and the tire cornering stiffnesses are considered varying. Since the external inputs (reference) and backlash-type hysteresis are both involved in the system model, we propose to design a weighted H ∞ gain-scheduling state-feedback controller. The feedback gains can be derived by solving a sequence of linear matrix inequalities. The relative importance of the steering system hysteresis and the reference signals can be tuned by a weighting factor. In addition, the gain-scheduling skill is employed to deal with the time-varying system parameters. Furthermore, the physical limitations of the actuators are considered by an eigenvalue placement technique. Simulation studies conducted in CarSim show that the proposed controller is capable of attenuating the effects of both steering system hysteresis and the time-varying parameters. Experimental results are also presented to demonstrate the effectiveness of this control algorithm in dealing with steering system backlash-type hysteresis when tracking yaw rate references.

2B31 Steering Control without Using Measurement of Lateral Velocity of Combination Vehicle(The 12th International Conference on Motion and Vibration Control)

In this paper, an adaptive steering controller for combination vehicles is proposed with using the wheel steering of semitrailer, the front and rear wheel steering of tractor without the measurement of lateral velocity of vehicle. The developed controller does not require any accurate knowledge of all vehicle parameters. The relative lateral position is measured and the controller is designed without using lateral velocity measurements. At first, we propose a new dynamic expression suitable for the design of a stable adaptive steering controller without using the lateral velocity. Next, we develop an ideal vehicle model as the reference vehicle model. In order to realize that the ideal vehicle model can have a good steering performance even when the drivers' steering characteristics vary, an evaluation function is introduced. Then, an ideal vehicle model is developed by using a trail and error approach so that the value of the evaluation function for the ideal vehicle model becomes smaller compared with that for the passive vehicle. At last, we develop an adaptive steering controller so that the behavior of the actual combined vehicle can track that of the ideal vehicle model. The effectiveness of the proposed steering controller is verified via computer simulations.

Low-complexity piecewise-affine virtual sensors: theory and design

This paper is focused on the theoretical development and the hardware implementation of low-complexity piecewise-affine direct virtual sensors for the estimation of unmeasured variables of interest of nonlinear systems. The direct virtual sensor is designed directly from measured inputs and outputs of the system and does not require a dynamical model. The proposed approach allows one to design estimators which mitigate the effect of the so-called ‘curse of dimensionality’ of simplicial piecewise-affine functions, and can be therefore applied to relatively high-order systems, enjoying convergence and optimality properties. An automatic toolchain is also presented to generate the VHDL code describing the digital circuit implementing the virtual sensor, starting from the set of measured input and output data. The proposed methodology is applied to generate an FPGA implementation of the virtual sensor for the estimation of vehicle lateral velocity, using a hardware-in-the-loop setting.

Implementation of EKF for Vehicle Velocities Estimation on FPGA

In order to improve the computational performance of the extended Kalman filter (EKF) for longitudinal and lateral vehicle velocities estimation, a novel scheme for the EKF implementation is proposed based on field programmable gate array (FPGA) and System on Programmable Chip (SoPC). A Nios II processor clocked at 100 MHz is embedded into the FPGA chip. The EKF is created by C/C++ program and runs in the Nios II processor. The main procedure for the EKF implementation using FPGA/SoPC technique is decomposed into three parts: system requirements analysis, hardware design, and software design. The proposed architecture offers favorable flexibility since it supports the reconfigurable hardware and reprogramming software. For the sake of increasing the computational efficiency, the single precision floating-point customized instructions and algorithm optimization are adopted. A testing platform is introduced to evaluate the functionality and the computational performance of the EKF, which includes an FPGA prototyping board and an xPC-Target system. Simulation results of standard double lane change, slalom test, and hard accelerating and braking test show that the proposed EKF implementation scheme has acceptable precision and computational efficiency.

Study on Integrated Control of Active Front Steering and Direct Yaw Moment Based on Vehicle Lateral Velocity Estimation

Considering the vehicle lateral velocity is difficult to be measured at integration of chassis control in configuration of production vehicle, this study presents the vehicle lateral velocity estimation based on the extended Kalman filtering with the standard sensor information. The fuzzy control algorithm is proposed to integrate direct yaw moment control and active front steering with lateral velocity estimation. The integration controller produces direct yaw moment and front wheel angle compensation to control yaw rate and sideslip angle, which makes the actual vehicle yaw rate and sideslip angle follow desirable yaw rate and desirable sideslip angle. The simulation results show vehicle handling and stability are enhanced under different driving cycles by the proposed algorithm.

Vehicle Velocity Observer Design Using 6-D IMU and Multiple-Observer Approach

This paper mainly focuses on the accurate estimation of the vehicle velocities of all axes, using the data received from a low-cost 6-D inertial measurement unit. The data include the vehicle linear acceleration and angular rates of all axes. In addition, the observer uses the wheel speed sensors and steering wheel angle information, which are already available on most recent production cars. Utilizing the aforementioned information, based on the combination of a bicycle model and a kinematic model, a multiple-observer system that computes the weighted sum estimation that is dependent on cornering stiffness adaptation is adopted to observe the lateral vehicle velocity, as well as longitudinal and vertical velocities. The stability of each component of the proposed observer is investigated, and a set of assessments to confirm the performance of the entire system is arranged through experiments using a real production sport utility vehicle.

Vehicle Lateral and Longitudinal Velocity Estimation Using Coupled EKF and RLS Methods

In order to meet the cost requirement of lateral and longitudinal velocity measured directly in vehicle active safety control systems, based on 3-DOF vehicle model and the Recursive Least Squares (RLS) which can identify the tire cornering stiffness online, a control algorithm using Extended Kalman Filter(EKF) to estimate lateral and longitudinal velocity is proposed. The estimation values are compared with simulator values from CarSim. The compared results demonstrated that the proposed algorithm could estimate the lateral and longitudinal velocity accurately and robustly.

Active control of lane keeping uses optic flow, bearing, and splay angle information

Previous studies have shown that people rely mainly on splay angle (the optical angle of the lane markers with respect to the vertical in the image plane) for lane keeping during driving (e.g., Beall & Loomis, Perception, 1996). Here we investigate whether optic flow and bearing also contribute to the control of lane keeping. The display (110° H × 94° V) simulated an observer steering a vehicle down a straight path at 3 m/s under three conditions: (a) an empty ground with lane markers providing bearing and splay angle information; (b) a random-dot ground with lane markers providing bearing, splay angle, and sparse optic flow information, and (c) a textured ground with lane markers providing bearing, splay angle, and dense optic flow information. Participants used a joystick to control the vehicle's lateral velocity to stay at the center of the lane while the vehicle's lateral position was perturbed by the sum of seven non-harmonic sinusoids (0.1 to 2.19 Hz). For 11 participants (9 na?ve), the vehicle's lateral deviation from the center of the lane, indicated by the RMS error, was larger for the empty ground (mean±SE: 0.39±0.02 m) than for the random-dot and the textured ground display (0.37±0.02 m in both cases), indicating the influence of optic flow on lane keeping. We then varied the optical gain of bearing in the display by providing lane markers (3.1± V) at three distances (3.2 m, 7.8 m, & 22.2 m). For eight participants (7 naïve), the RMS error increased with the distance for the empty ground but not for the random-dot or the textured ground display, indicating that bearing information helps lane keeping in the absence of optic flow information. We conclude that optic flow, bearing, and splay angle all contribute to accurate lane keeping.

A dynamic interactive vehicle model for modelling traffic beyond the microscopic level

Recent developments of inter-vehicular wireless communication technologies have motivated many innovative applications aiming at significantly increasing traffic throughput and improving highway safety. Powerful traffic simulation is an indispensable tool for developing these applications and studying their effects before large-scale deployment. One of the necessary advances to such a tool is to incorporate vehicle dynamics in traffic simulation so that the effects of improved driving strategies and cooperation between drivers can be examined at a great level of detail. Existing dynamic vehicle models, owing to their complexity for high modelling fidelity, are not suited for efficient traffic simulation involving many vehicles. To fill the gap, we propose a simple Dynamic Interactive Vehicle (DIV) model that requires little calibration effort and fewer computational resources. The DIV model takes as input a driver's desired acceleration, deceleration and steering, and outputs vehicle dynamic responses, including longitudinal and lateral accelerations and yaw velocity.

On non-linear unknown input observers–applied to lateral vehicle velocity estimation on banked roads

Unknown input observers (UIOs) are observers that have stable error dynamics that are independent of unknown inputs. This paper studies such observers for non-linear systems, and shows that the error dynamics for a non-linear UIO has the same structure as the error dynamics of a non-linear observer without unknown inputs. This result is first used to provide synthesis inequalities for UIOs for a class of non-linear systems, and secondly, to inspire the design of an observer for estimation of vehicle lateral velocity on banked roads.

The identifying extended Kalman filter: Parametric system identification of a vehicle handling model

This article considers a novel method for estimating parameters in a vehicle-handling dynamic model using a recursive filter. The well-known extended Kalman filter - which is widely used for real-time state estimation of vehicle dynamics - is used here in an unorthodox fashion; a model is prescribed for the sensors alone, and the state vector is replaced by a set of unknown model parameters. With the aid of two simple tuning parameters, the system self-regulates its estimates of parameter and sensor errors, and hence smoothly identifies optimal parameter choices. The method makes one contentious assumption that vehicle lateral velocity (or body sideslip angle) is available as a measurement, along with the more conventionally available yaw velocity state. However, the article demonstrates that by using the new generation of combined GPS/inertial body motion measurement systems, a suitable lateral velocity signal is indeed measurable. The system identification is thus demonstrated in simulation, and also proved by successful parametrization of a model, using test vehicle data. The identifying extended Kalman filter has applications in model validation - for example, acting as a reference between vehicle behaviour and higher-order multi-body models - and it could also be operated in a real-time capacity to adapt parameters in model-based vehicle control applications.

Vehicle dynamics and external disturbance estimation for vehicle path prediction

This paper addresses the onboard prediction of a motor vehicle's path to help enable a variety of emerging functions in autonomous vehicle control and active safety systems. It is shown in simulation that good accuracy of path prediction is achieved using numerical integration of a linearized two degree of freedom vehicle handling model. To improve performance, a steady-state Kalman filter is developed to estimate the vehicle's lateral velocity and the magnitudes of external disturbances acting on the vehicle, specifically the lateral force and the yaw moment disturbances. A comparison is made between three models of external disturbance time variation; a piecewise-constant-in-time model is found to be sufficient. Finally, an algorithm is proposed to characterize path prediction uncertainty using a statistical characterization of the measurement and modeling errors. Simulation suggests that these algorithms may provide a useful suite of path prediction tools for a variety of applications.

Passivity-Based Nonlinear Observer for Lateral Control of Tractor-Trailer Vehicles in Automated Highway Systems

A nonlinear observer is proposed to estimate vehicle lateral velocity and yaw rates for lateral motion control of tractor-trailer vehicles in automated highway systems. The design of this observer is based on the passivity and damping properties inherent in vehicle systems. Simulation results show the effectiveness of this nonlinear observer.

Vehicle Lateral Velocity and Yaw Rate Control With Two Independent Control Inputs

In automatic lateral control, which will play a key role in highway automation, vehicles must follow a given path and vehicle direction must be controlled as desired. Therefore, the ideal is for the lateral motion and yaw motion of the vehicle to be controlled independently. This requires at least one additional control input which is independent of the front steering angle. This paper explores the use of two independent control inputs, which are the front steering angle and an extra control input. Three types of extra control inputs are considered: (1) a differential driving torque between the two front wheels; (2) that between the two rear wheels; and (3) the rear steering angle. The analysis utilizing a linearized vehicle model shows that the front and rear independent steering allows a wider variation of lateral velocity and yaw rate in the steady state. A control algorithm with front and rear independent steering, which features continuously changing gains dependent on vehicle velocity and road conditions, is presented. Performance evaluation is based on a simulation study on a nonlinear vehicle model and an experimental study with a laboratory model vehicle.

An Optical System for Manned Vehicle Terminal Guidance

An optical system for implementing manned vehicle terminal guidance for a soft lunar landing is described. The method utilizes an on-board tracking telescope to establish azimuth and elevation angles. With altitude, this system provides sufficient information for computing lateral vehicle velocity which can be controlled within less than 2 ft/sec, a value not to be exceeded for a safe landing.