Vehicle Lateral Velocity Research Articles

Hybrid Feature Selection Approach to Finding Optimal Feature Subsets for Vehicle Lateral Velocity Estimation

Hybrid Feature Selection Approach to Finding Optimal Feature Subsets for Vehicle Lateral Velocity Estimation

Nonlinear observer-based adaptive control of ground vehicles with uncertainty estimation

In this work, a nonlinear observer-based controller is designed for the attitude control of a ground vehicle with uncertainty estimation. This controller ensures the tracking of desired references in the presence of environmental disturbances and parametric uncertainties, making use of high-order sliding-mode estimators to estimate these perturbations. Furthermore, the controller incorporates a kinematic observer reconstructing the lateral vehicle velocity, based on a novel high-order sliding-mode observer. The proposed controller ensures good performance and better transient behavior with respect to existing controllers when parametric uncertainties and environmental disturbances are present. Its performance is evaluated by numerical simulation in CarSim, considering a challenging double turn maneuver, which is described in the ISO-3888 specifications.

Research on Fault-Tolerant Control of Distributed-Drive Electric Vehicles Based on Fuzzy Fault Diagnosis

This paper addresses the fault problem in distributed-four-wheel-drive electric vehicle drive systems. First, a fault-factor-based active fault diagnosis strategy is proposed. Second, a fault-tolerant controller is designed to reconstruct motor drive torque based on vehicle stability. This controller ensures that the vehicle maintains stability by providing fault-free motor output torque based on fault diagnosis results. To validate the effectiveness of the fault diagnosis and fault-tolerant control, SIL simulation is conducted using MATLAB/Simulink and CarSim. A hardware-in-the-loop (HIL) simulation platform with the highest confidence level is established based on NI PXI and CarSim RT. Through the HIL simulation experiments, it is shown that the proposed control strategy can accurately diagnose the operating state of the motor, rebuild the motor torque based on stability, and demonstrate robust stability when the drive system fails. Under various fault conditions, the maximum error in the vehicle lateral angular velocity is less than 0.017 rad/s and the maximum deviation in the lateral direction is less than 0.7 m. These findings substantiate the highly robust stability of the proposed method.

Vehicle Sideslip Angle Estimation Based on Interacting Multiple Model Kalman Filter Using Low-Cost Sensor Fusion

This study proposes a new method for vehicle sideslip angle estimation utilizing the competitively priced sensor fusion using in-vehicle sensors and low-cost standalone global positioning system (GPS). To estimate unmeasurable vehicle states, vehicle sideslip angle and tire cornering stiffness, an interacting multiple model (IMM) Kalman filter is proposed that combines two extended Kalman filters (EKFs), each including kinematic and dynamic equations of vehicle lateral velocity. To properly combine the outputs of these model-based EKFs, a weighted probability of each model based on the stochastic process is designed, which reflects the characteristics of each of the kinematic and dynamic equations in real-time. Also, the observability of the proposed estimation algorithm is checked by observability functions of nonlinear systems. The estimation performance in various driving scenarios is verified using an experimental vehicle, and its superiority is confirmed through a comparative study. The proposed algorithm makes the following main contributions for estimating the vehicle sideslip angle: 1) the high optimality of estimation results and 2) the accurate estimation of tire cornering stiffness.

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.

Nonlinear Time-Delay Observer-Based Control to Estimate Vehicle States: Lateral Vehicle Model

This paper deals with the state estimation and control problem for nonlinear lateral vehicle dynamics with time delays. First, a novel time-varying delay vehicle model described as a Takagi-Sugeno fuzzy model is presented. In particular, it is considered that the lateral force contains an air resistance term which is assumed to be a quadratic function of the lateral vehicle velocity. A time-varying delay has been included in the vehicle states by a simple formula in order to capture brake actuation aspects or other practical aspects that may generate a delayed response, while the nonlinear part of the vehicle model is described as a Lipschitz function. A Takagi-Sugeno time-delay observer-based control that satisfies the Lipschitz condition is proposed to get closed-loop stability conditions. These results generalize existing ones in the literature on lateral dynamics control. Additionally, we provide a new methodology for the controller and observer gains design that can be cast as linear matrix inequality constraints. Finally, we illustrate our results with numerical examples, which also reveal the negative effect of not considering the presence of delays in the controller design.

Personalized path generation and robust H ∞ output‐feedback path following control for automated vehicles considering driving styles

This paper proposes a personalized output-feedback path-following control strategy for automated vehicles. A personalized path generation approach is created to obtain the expected paths of drivers with different driving styles. Historical driving data and road features are used to calculate the waypoints that characterize driving styles. Then, a robust H ∞ output-feedback controller is designed to follow the generated paths. A solution based on matrix partition is provided to compute the output-feedback control gains without using the vehicle lateral velocity signal, which facilitates the practical implementation of the designed controller. The proposed driving strategy is validated in CarSim® simulations and scaled car experiments. The results show the personalized paths can match with drivers’ driving styles and can be tracked by the designed output-feedback path-following controller.

Design of a lateral velocity observer based on synthesis of different sources

This paper is mainly devoted to estimating the lateral velocity of vehicle with an observer synthesis method using a cost-effective 6D inertial measurement unit. The four sources of observer to be synthesized come from the dynamics model, the kinematics model, and a LuGre tire model, respectively. The weight of each source is calculated by considering the nonlinear extent of each tire by means of the adaptation of tire cornering stiffness, which is adapted and updated using the dynamics model observer with the information obtained from the kinematics model observer. Then using the weighted sum of the four sources as the reference information for a modified kinematics observer, the final estimation can provide smooth and accurate results that are robust to sensor drifts and measurement noise, which is verified with several dSPACE based hardware-in-the-loop simulations.

Study on the influence of lateral and local rail deformation on the train–track interaction dynamics

This paper studies the influence of lateral and local rail deformation on the train–track interaction dynamics in high-speed railways. While the traditional method ignores the curvature of a laterally and locally deformed rail and treats it as a straight rail with an additional displacement, this paper considers the laterally and locally deformed rail as a curved rail and models the train–track interaction with lateral and local rail deformation using a reduced beam model with necessary changes to fit the curved rail. Thus, the presented model is more realistic and can accurately and efficiently analyses the influence of lateral and local rail deformation on the train–track interaction dynamics. After calculation of dynamic responses of train–track interaction with lateral and local rail deformation with and without rail irregularities, the influence of the amplitude of lateral and local rail deformation and vehicle velocity on transient derailment criteria is analysed, and the derailment boundary is finally obtained. It is found that the traditional method underestimates dynamic responses of train–track interaction with lateral and local rail deformation and overestimates the derailment boundary of the vehicle, and the risk of derailment and rail damage increases with the increasing amplitude of the lateral and local rail deformation.

A Reliable Vehicle Lateral Velocity Estimation Methodology Based on SBI-LSTM During GPS-Outage

Vehicle lateral velocity is critical for the high-level automatic driving technology, andyyfusing Global Position System (GPS) in the lateral velocity estimation method can greatly improve the estimation accuracy. However, the method fusing GPS is seldom reported, since the problem of GPS-outage often appears. Accordingly, this article proposes a novel lateral velocity estimation method (VLVEM) based on SBI-LSTM in GPS-outage environment. Additionally, VLVEM integrating Inertial Navigation System-aided GPS (INS-aided GPS) is derived from Federated Kalman Filter (FKF) algorithm. Furthermore, during GPS-outage, induced from the Stack Bidirectional Long Short-Term Memory Recurrent Neural Network (SBI-LSTM RNN), an INS-aided GPS fault reconstructor (IGFR) is designed to reconstruct INS-aided GPS model. Finally, the simulation results show that compared with most of the existing methods which only consider the in-vehicle sensors' signals, the proposed method fusing GPS has higher lateral velocity estimation accuracy. Besides, when GPS-outage causes INS-aided GPS failure, IGFR can reconstruct INS-aided GPS model, and VLVEM still has high estimation accuracy. Combining the in-vehicle sensors and GPS, VLVEM exhibits great robustness and fault tolerance.

Rear-Wheel Steering Control for Enhanced Steady-State and Transient Vehicle Handling Characteristics

This paper presents a rear-wheel steering (RWS) control algorithm to enhance the vehicle handling performance without prior knowledge of tire characteristics. A RWS system is a chassis control module that can effectively improve vehicle maneuverability and stability. Since the tire-road friction coefficient is difficult to obtain in real world application, the proposed RWS control algorithm is designed so that it can be implemented without any tire-road information. The proposed RWS control algorithm consists of steady-state and transient control inputs. The steady-state control input is proportional to the driver's steering input for achieving the desired yaw rate gain. The desired yaw rate gain is obtained through an offline optimization that is aimed to minimize the vehicle lateral velocity. The transient control input consists of feedforward and feedback control inputs. The feedforward input is designed to improve transient responses of the yaw rate. Computer simulation studies have shown that a trade-off relationship between overshoot and response time exists when the RWS control input is a sum of the steady-state and feedforward inputs. To compromise this conflict, a feedback input has been designed. The overshoot can be significantly reduced while the response time is slightly changed via the feedback input. The proposed algorithm has been investigated via computer simulations. The simulation has been conducted for step steer and sine with dwell scenarios under various road friction conditions. The performance of a RWS vehicle was evaluated using objective indices. Simulation results show that the proposed algorithm enhances vehicle handling performance.

Human-Centered Trajectory Tracking Control for Autonomous Vehicles With Driver Cut-In Behavior Prediction

Trajectory tracking control in the cut-in scenarios is challenging, since the autonomous vehicles have to follow the reference trajectory and cooperate with the cut-in vehicles. This paper proposes a human-centered trajectory tracking control strategy integrating driver behavior prediction for the cut-in scenarios and their transient processes. A recurrent neural network (RNN) with long short-term memory (LSTM) cells is used to predict the driver behaviors of the cut-in vehicle. Then, a model predictive control (MPC) approach considering the driver behaviors of the cut-in vehicle is designed to track the reference trajectory. The transient processes of the cut-in scenarios are considered for different cut-in behaviors. Moreover, the moving horizon estimator (MHE) is used to estimate the vehicle lateral velocity that is used in the controller. Human driver tests on a driving simulator show that the drivers’ intention of the cut-in vehicle can be predicted by the RNN with LSTM cells. CarSim® simulation studies show the human-centered trajectory tracking controller can track the reference trajectory using the estimated vehicle lateral velocity. The autonomous vehicle can cooperate with the cut-in vehicle in different driving situations and obtain smooth transient processes of the cut-in scenarios.

Robust H∞ state-feedback control for path following of autonomous ground vehicle based on lateral velocity estimation

A robust [Formula: see text] observer-based static state-feedback controller is designed for the path following of autonomous ground vehicles in this paper. Considering the lateral velocity of vehicle is usually difficult to measure, an observer is designed to estimate the value of lateral velocity first. Then, a robust controller is proposed to cope with the modeling uncertainty and disturbance, such as the variation of road coefficient and lateral disturbance. Numerical simulations using a high-fidelity and full-vehicle model based on a CarSim–Simulink joint platform have verified the effectiveness of the proposed approach.

EV Traction Control Based on Nonlinear Observers Considering Longitudinal and Lateral Tire Forces

An observer-based traction control strategy for electric vehicles is proposed in this paper. The proposed strategy considers the combined effects of lateral and longitudinal traction forces, both for acceleration and regenerative braking, even in curved trajectories. Nonlinear reduced-order observers are designed for estimating tire-road friction condition on each traction wheel and vehicle lateral velocity from which side-slip angles are calculated. A detailed analysis of observers convergence is performed, and a method to know the quality of the estimated variables is also proposed. The proposed traction control strategy allows avoiding the traction wheels skidding during acceleration and braking both in straight trajectories and turning maneuvers. The performance of the proposal is verified through simulation on a complete vehicle model, under different situations and even considering a different vehicle tire model.

Robust H∞ dynamic output-feedback control for four-wheel independently actuated electric ground vehicles through integrated AFS/DYC

This paper simultaneously addresses the parameter/state uncertainties, external disturbances, input saturations, and actuator faults in the handling and stability control for four-wheel independently actuated (FWIA) electric ground vehicles (EGVs). Considering the high cost of the available sensors for vehicle lateral velocity measurement, a robust H∞ dynamic output-feedback controller is designed to control the vehicle motion without using the lateral velocity information. The investigated parameter/state uncertainties include the tire cornering stiffness, vehicle mass, and vehicle longitudinal velocity. The unmodeled terms in the vehicle lateral dynamics model are dealt as the external disturbances. Faults of the active steering system and in-wheel motors can cause dangerous consequences for driving, and are considered in the control design. Input saturation issues for the tire forces can deteriorate the control effects, and are handled by the proposed strategy. Integrated control with active front steering (AFS) and direct yaw moment (DYC) is adopted to control the vehicle yaw rate and sideslip angle simultaneously. Simulation results based on a high-fidelity and full-car model via CarSim-Simulink show the effectiveness of the proposed control approach.

Integrated model predictive control and velocity estimation of electric vehicles

In this paper, an integrated estimation and control system is developed for the stability and traction control of electric vehicles. A model predictive control technique is used to track the desired vehicle yaw rate while maintaining small lateral velocity and tire slip ratios. This paper proposes a new method to control the lateral stability of the vehicle. In this method, the lateral vehicle velocity is controlled indirectly by adjusting the reference yaw rate. This reduces the size of the prediction model and its computational complexity. The controller requires the vehicle’s lateral and longitudinal velocities as well as its tire forces for stability and traction control. This paper also proposes a novel velocity estimation scheme that uses the combined vehicle kinematics and tire model. The developed Kalman-based estimator provides velocities and lateral forces at each corner that are robust to changes in the road condition. The combined model-based and kinematic-based estimation structure mitigates some common problems of the widely used kinematic-based estimators such as the spikes and drifting issues. The stability of the proposed time-varying estimator is also investigated. The designed control and estimation scheme are experimentally validated on various driveline configurations and proven to provide reliable results.

A New Approach for Vehicle Lateral Velocity and Yaw Rate Control with Uncertainty

AbstractIn order to attain excellent stability and maneuverability to ensure safety and ride‐comfort, the lateral velocity and yaw rate of a vehicle are expected to be controlled at any desired values simultaneously. A basic manipulation model of a vehicle with two degrees of freedom which requires two independent control inputs (front and rear steering angle) is constructed. In this model, we consider the mass and the moment of inertia of the vehicle are the uncertain parameters which are (possibly) fast‐varying. However, no further information, except that the uncertainties are bounded, is assumed. Furthermore, the bound is unknown. An adaptive robust control methodology based on the Udwadia and Kalaba approach which guarantees uniform boundedness and uniform ultimate boundedness is proposed to drive the system to follow the pre‐specified constraints approximately. The adaptive law is of leakage type which can adjust itself based on the tracking error. The numerical simulation results conducted by MATLAB demonstrate the ease and effectiveness of implementation.

Simultaneous Vehicle Real-Time Longitudinal and Lateral Velocity Estimation

Accurate information on vehicle longitudinal and lateral velocities is vital for the efficient operation of many vehicle control systems. In this paper, an estimation structure to simultaneously estimate vehicle velocities in longitudinal and lateral directions is developed and experimentally validated. This structure includes two parallel estimators: The first estimator is a kinematic-based observer for longitudinal velocity estimation, and the second is a combination of a kinematic-based observer and an inverse tire model to estimate vehicle lateral velocity. The proposed structure can effectively handle the additive biases, which are common in a vehicle's stock accelerometers' signals, and provide an accurate estimate of vehicle velocities when one (or more than one) wheel experiences excessive slippage. Additionally, the proposed structure is not sensitive to changes in parameters of the tire model and vehicle mass. The performance of this estimation structure is validated by experimental studies.

Nonlinear adaptive tracking for ground vehicles in the presence of lateral wind disturbance and parameter variations

In this paper we design a dynamic controller for the attitude control of a ground vehicle. This controller ensures the tracking of desired references, in the presence of uncertainties in the parameters describing the tire-road friction coefficient and the lateral tire stiffness, and in the presence of external perturbations, such as lateral wind. It incorporates some dynamics reconstructing the lateral vehicle velocity, often not directly measured. Finally, the proposed control scheme allows the identification of the unknown parameters and, at the same time, of the disturbance due to the lateral wind acting on the vehicle. The performance of the dynamic controller is evaluated making use of CarSim considering a challenging double steer maneuver.

Robust output-feedback based vehicle lateral motion control considering network-induced delay and tire force saturation

This paper presents a robust H∞ output-feedback vehicle lateral motion control strategy considering network-induced delay and tire force saturation. The unavoidable time delay in the in-vehicle networks degrades the control performance, and even deteriorates the system stability. In addition, the tire lateral force suffers saturation phenomenon, which also deteriorates the control effect in extreme driving conditions. To handle the network-induced control delay and tire force saturation, a robust H∞ controller is presented to regulate the vehicle lateral motion. An output-feedback control schema, which does not need the vehicle lateral velocity, is designed to achieve the desired control performance and reduce the cost of control system. The tire cornering stiffness uncertainty and external disturbances are also considered in the controller design to improve the robustness of the proposed controller. The comparative simulation results based on Carsim-Simulink joint simulation verify the effectiveness and robustness of the proposed control strategy.