Vehicle State Estimation Research Articles

Development of vehicle test system and vehicle state estimation based on GPS/INS integrated navigation system

Development of vehicle test system and vehicle state estimation based on GPS/INS integrated navigation system

Disturbance Compensation and Torque Coordinated Control of Four In-Wheel Motor Independent-Drive Electric Vehicles

Functional overlaps and conflicts between Electronic Power Steering (EPS) and Torque Vectoring Control (TVC) for Distributed Drive Electric Vehicles (DDEVs) are issues that need to be addressed urgently. This paper deals with the interaction between EPS and TVC from the view of the influence mechanism and control objectives. The disturbance steering torque caused by the differential torque is estimated and a unified steering characteristic is proposed. To validate the analysis results, a torque compensation strategy is modified based on the exiting control system. And a robust vehicle state estimator is built thanks to the aligning torque estimation to provide the necessary information. Typical ground test results show that the torque oscillation on the steering wheel is suppressed. A faster and more linear steering response can be seen, which means that the proposed disturbance compensation strategy can comfort the contradiction between EPS and TVC, also improve the handling performance of the vehicle.

Set-membership Switched Observers based on Interval Characterization of the Estimation Error

This paper presents a new set-membership estimation methodology for uncertain switched LPV discrete-time systems subject to unknown inputs, unmeasurable time-varying parameters and measurement noise. The proposed approach provides a guaranteed interval that is constructed as the sum of punctual state estimation and its corresponding estimation error limits. First, a punctual switched unknown input observer, robust against unknown inputs and time-varying uncertainties, is constructed. The proposed switched observer design is based on the solution of an optimization problem in terms of LMIs. Then, an outer-approximation of the enclosure set of state estimation error is computed using the admissible bounds of state and uncertainties. Application to vehicle state estimation is provided to show the design procedure and the flexibility of the proposed scheme. Comparison to real data demonstrates the accuracy and effectiveness of the obtained results.

Toward Accurate Vehicle State Estimation Under Non-Gaussian Noises

Vehicle state including location and motion information plays an important role in various applications such as Internet of Vehicles (IoV), autonomous cars, and driving safety monitoring. Achieving accurate vehicle state is a challenging task in those applications due to the noise disturbances. Recent studies suggest that noise is not generally Gaussian distributed and many physical environments can be handled more accurately as non-Gaussian rather than Gaussian model. Inspired by this observation, we strive to improve the vehicle state estimation by investigating the effects of that assumption when process and measurement noises are non-Gaussian distributed. Here, process noise represents the noise during the state information processing. To that end, we exploit the generalized error distribution (GED) to compute the non-Gaussian probability density during the vehicle state estimation. We then derive extensive theoretical analysis targeting to estimate the parameters such as the mean and the variance (or covariance matrix) related to both process and measurement noises and reduce the computational burden of the distribution. Further, we propose a non-Gaussian particle filter for vehicle state estimation ( ${n}$ GPF-VSE) algorithm wherein we utilize the genetic operator resampling (GOR) technique to enhance the efficiency of particle filter (PF) relying on the selection of the importance sampling distribution. To evaluate the performance of the proposed approach, we conduct numerical simulations on the popular system of state-space equations and a real experiment for estimating the vehicle state. The results from the numerical simulations, experimental data and the statistical evaluation confirm that ${n}$ GPF-VSE outperforms existing methods in terms of vehicle state accuracy.

Trajectory tracking for autonomous vehicles on varying road surfaces by friction-adaptive nonlinear model predictive control

ABSTRACTWe propose an adaptive nonlinear model predictive control (NMPC) for vehicle tracking control. The controller learns in real time a tyre force model to adapt to a varying road surface that is only indirectly observed from the effects of the tyre forces determining the vehicle dynamics. Learning the entire tyre model from data would require driving in the unstable region of the vehicle dynamics with a prediction model that has not yet converged. Instead, our approach combines NMPC with a noise-adaptive particle filter for vehicle state and tyre stiffness estimation and a pre-determined library of tyre models. The stiffness estimator determines the linear component of the tyre model during normal vehicle driving, and the control strategy exploits a relation between the tyre stiffness and the nonlinear part of the tyre force to select the appropriate full tyre model from the library, which is then used in the NMPC prediction model. We validate the approach in simulation using real vehicle parameters, demonstrate the real-time feasibility in automotive-grade processors using a rapid prototyping unit, and report preliminary results of experimental validation on a snow-covered test track.

Reliable State Estimation of an Unmanned Aerial Vehicle Over a Distributed Wireless IoT Network

Unmanned aerial vehicles (UAVs) have attracted a lot of attention due to their enormous potentiality in civil and military applications over the past years. In order to allow accurate control action of UAV, a robust and real-time state estimation technique is required. In this paper, we propose a Kalman filter based UAV state estimation technique when the communication takes place over wireless links in an Internet of Things (IoT) network. We consider that a set of sensors observes the state of the UAV and transmits the observation to a control center (central server) over a distributed wireless IoT network. To deal with the communication impairments due to wireless communication links between the UAV's sensors and the IoT system components, e.g., IoT gateways, a Bose–Chaudhuri–Hocquenghem coded communication system is presented. Based on the received signals at the IoT gateways, a global state estimation technique is proposed. Performance of the proposed communication and estimation scheme is demonstrated through numerical results for different conditions. From the comparison with a conventional estimation scheme, it is observed that the proposed scheme significantly outperforms the conventional scheme in terms of state estimation and error performance.

Design of an Interacting Multiple Model-Cubature Kalman Filter Approach for Vehicle Sideslip Angle and Tire Forces Estimation

Vehicle states estimation (e.g., vehicle sideslip angle and tire force) is a key factor for vehicle stability control. However, the accurate values of these parameters could not be obtained directly. In this paper, an interacting multiple model-cubature Kalman filter (IMM-CKF) is used to estimate the vehicle state parameters. And improvements about estimation method are achieved in this paper. Firstly, the accuracy of the reference model is improved by building two different models: one is 7-degree-of-freedom (7 DOF) vehicle model with linear tire model, and the other is 7 DOF vehicle model with nonlinear Dugoff tire model. Secondly, the different models are switched by IMM-CKF to match different driving condition. Thirdly, the lateral acceleration correction for sideslip angle estimation is considered, because the sensor of lateral acceleration is easy to be influenced by the gravity on banked road. Then, to compare cubature Kalman filter (CKF) estimation method and IMM-CKF estimation method Hardware-In-Loop (HIL) tests are carried out in the paper. And simulation results show that IMM-CKF methodology can provide accurate estimation values of vehicle states parameters.

Advanced Vehicle State Monitoring: Evaluating Moving Horizon Estimators and Unscented Kalman Filter

Active safety systems must be used to manipulate the dynamics of autonomous vehicles to ensure safety. To this end, accurate vehicle information, such as the longitudinal and lateral velocities, is crucial. Measuring these states, however, can be expensive, and the measurements can be polluted by noise. The available solutions often resort to Bayesian filters, such as the Kalman filter, but can be vulnerable and erroneous when the underlying assumptions do not hold. With its clear merits in handling nonlinearities and uncertainties, moving horizon estimation (MHE) can potentially solve the problem and is thus studied for vehicle state estimation. This paper designs an unscented Kalman filter, standard MHE, modified MHE, and recursive least squares MHE to estimate critical vehicle states, respectively. All the estimators are formulated based upon a highly nonlinear vehicle model that is shown to be locally observable. The convergence rate, accuracy, and robustness of the four estimation algorithms are comprehensively characterized and compared under three different driving maneuvres. For MHE-based algorithms, the effects of horizon length and optimization techniques on the computational efficiency and accuracy are also investigated.

Development and evaluation of torque distribution methods for four-wheel independent-drive electric vehicle based on parameter estimation

A hierarchical controller including a vehicle condition parameter estimator for improving the vehicle stability is theoretically applied to a four-wheel independent-drive electric vehicle. This study is aimed at combining the parameters estimation with vehicle stability control and evaluating different optimization algorithms based on the computation time and fluctuation. The hierarchical controller consists of a vehicle condition parameter estimator, active yaw moment controller, and torque distribution controller. The vehicle condition parameter estimator is based on the Unscented Kalman Filter, which avoids the truncation error caused by Taylor expansion to convert a nonlinear system into a linear system. The active yaw moment controller is designed based on the sliding mode control. The controller employs co-control between the vehicle sideslip angle and yaw rate, which properly accounts for both factors. The objective function of the torque distribution controller is based on the tire utilization function and constraint condition. The torque distribution and wheel slip rate are accounted for simultaneously. The torque distribution methods are tested based on nonlinear programming, quadratic programming, and weighted least squares for the sake of comparison. The simulation results demonstrate the accuracy of the proposed vehicle state estimator and the effectiveness of the proposed stability controller.

Adaptive Neuro-Fuzzy Predictor-Based Control for Cooperative Adaptive Cruise Control System

In this paper, an adaptive neuro-fuzzy predictor-based control (ANFPC) approach with integrated automotive radar and vehicle-to-vehicle (V2V) communication for the design of the cooperative adaptive cruise control (CACC) system is presented, which concerns not only the safety and riding comfort of a vehicle but also enhances its fuel efficiency. This paper consists of two main parts: preceding vehicle state estimation and following vehicle controller. First, the prospective information of the preceding vehicle, such as position, velocity, and acceleration, can be derived through radar sensor, and the control force of the preceding vehicle can be transmitted to the following vehicles through V2V communication. A Takagi–Sugeno fuzzy model is then utilized to estimate the preceding vehicle model, and the predicted state sequence of the preceding vehicle can be obtained. Second, based on these predicted data, the following vehicle is controlled by the proposed ANFPC scheme to maintain each vehicle within the desired distance headway and thus achieve string stability of vehicle platooning and fuel efficiency. The experimental results on the CarSim environment show that the proposed control strategy for the CACC system can significantly reduce the fuel consumption while ensuring driving comfort and safety.

Design of Vehicle Running States-Fused Estimation Strategy Using Kalman Filters and Tire Force Compensation Method

Accurate and reliable vehicle state estimation results are very significant to the active safety, energy optimization, and the intelligent control of vehicles. In this paper, to improve the accuracy and adaptability of vehicle running state estimation, the vehicle running states fused estimation strategy is presented for in-wheel motor drive electric vehicle using the Kalman filters and tire force compensation method. The concept of electric drive wheel model (EDWM) is developed and deduced, and then, considering that the EDWM is a nonlinear model with an unknown input, the design concept of high-order sliding mode observer is used to construct the state space equation of longitudinal force. To improve the accuracy and the reliability of vehicle state estimation, an overall estimation strategy with information fusion and tire force compensation is designed, in which a weighted square-root cubature Kalman filter with an adaptive covariance matrix of measurement noise is developed for observer design. Finally, the simulations in CarSim-Simulink co-simulation model and experiments are carried out, and the effectiveness of the designed estimation strategy is validated.

Vehicle state estimation based on PSO-RBF neural network

In the last few years, many closed-loop control systems have been introduced in the automotive field to increase the level of safety and driving automation. For the integration of such systems, it ...

Integrated state and parameter estimation for vehicle dynamics control

Most modern day automotive chassis control systems employ a feedback control structure. Therefore, a real-time estimate of the vehicle handling dynamic states and tyre-road contact parameters are invaluable for enhancing the performance of current vehicle control systems, such as anti-lock brake system (ABS) and electronic stability program (ESP). Today's production cars are equipped with onboard sensors (e.g., a 3-axis accelerometer, 3-axis gyroscope, steering wheel angle sensor, and wheel speed sensors) which when used in conjunction with certain model based observers can be used to identify relevant vehicle states for optimal control of comfort, stability and handling. However, some key variables such as the tyre forces, road bank/grade angles, and the tyre-road friction coefficient, which have a significant impact on vehicle handling performance and safety are difficult to measure using sensors already onboard vehicles. This paper introduces an integrated vehicle state estimator comprising a series of model-based and kinematic-based observers for estimating these unmeasurable states. Using an appropriate vehicle model, kinematic equations of motion and vehicle sensor data, the unknown vehicle states as well as the tyre-road contact forces are estimated by implementing a series of observers arranged in a cascade structure. Key estimated signals include the vehicle side slip angle (β), tyre longitudinal/lateral/vertical forces, and the tyre-road friction coefficient (μ). The performance of the proposed estimators has been evaluated via computer simulations conducted using the vehicle dynamics software CarSim®. An effectively designed merging scheme ensures robust estimation performance even during the vehicle manoeuvres which show highly nonlinear tyre characteristics and in the existence of road inclination or bank angle.

State and parameter estimation based on a modified particle filter for an in-wheel-motor-drive electric vehicle

This paper presents a modified particle filter (MPF) to estimate vehicle states and parameter with high precision and robustness under complex noises and sensor fault conditions. To deal with the particle impoverishment issue, the vector particle swarm of the multivariable system is separated into univariate particle swarms, which are diversified with the selection, crossover and mutation operations of the genetic algorithm (GA) while maintaining the mean value and enlarging the standard deviation. The effectiveness of the proposed estimation scheme is verified under the scenarios of the stochastic and needling noises and acceleration sensor faults through the Carmaker-Simulink joint simulations based on typical maneuvers, outperforming the commonly-used vehicle state estimators including the unscented Kalman filter (UKF) and the unscented particle filter (UPF).

Vision-Based Lateral State Estimation for Integrated Control of Automated Vehicles Considering Multirate and Unevenly Delayed Measurements

Integrated position and motion control are critical for future automated vehicles, but the sampling sequences of the two control loops are different. Normally, the position-related measurements from an onboard vision system are infrequent and with delay. Aiming at improved position states estimation, this paper develops a multisensor fusion estimation scheme based on a vision-vehicle model. Different from conventional methods such as state augmentation, an algorithm solving the sensor sampling mismatch issues in a single step is proposed. To design the estimator, a combined vision-vehicle model for lateral vehicle state estimation is first introduced, and the multirate and uneven sampling delay problems are explained from the perspectives of sensor fusion. Then, a measurement reconstruction algorithm with intersample compensation is introduced to solve the sensor measurement mismatch issue. Using this method, vehicle lateral states such as lateral position to the lane and heading angle can be updated faster and more reliably. Performance of the proposed lateral state estimation algorithm is verified by field test data.

SE(2)-Constrained Visual Inertial Fusion for Ground Vehicles

Visual inertial fusion is the problem to estimate vehicle states using visual and inertial measurements, and has recently been an active research topic. Existing visual inertial fusion approaches formulate their estimation algorithms for mobile robots in general 3-D space, and cannot satisfactorily model the nearly planar motion of ground vehicles. This paper proposed a new visual inertial fusion framework for ground vehicles, based on an SE(2)-constrained pose parameterization, which maximally conforms to the realistic situations of the ground vehicle motion. The estimation system is formulated as a graph optimization problem, and the SE(2)-constrained parameterization is implemented by introducing specially designed edges on the vehicle poses in the graph. Keyframe-based optimization and marginalization help to ensure the real-time performance. Experimental tests were conducted on challenging data sets with illumination changes and sharp-turn motion to validate the better accuracy of the proposed system compared with the state-of-the-art visual inertial estimation approaches.

Literature review and fundamental approaches for vehicle and tire state estimation

ABSTRACTMost modern day automotive chassis control systems employ a feedback control structure. Therefore, real-time estimates of the vehicle dynamic states and tire-road contact parameters are invaluable for enhancing the performance of vehicle control systems, such as anti-lock brake system (ABS) and electronic stability program (ESP). Today's production vehicles are equipped with onboard sensors (e.g. a 3-axis accelerometer, 3-axis gyroscope, steering wheel angle sensor, and wheel speed sensors), which when used in conjunction with certain model-based or kinematics-based observers can be used to identify relevant tire and vehicle states for optimal control of comfort, stability and handling. Vehicle state estimation is becoming ever more relevant with the increased sophistication of chassis control systems. This paper presents a comprehensive overview of the state-of-the-art in the field of vehicle and tire state estimation. It is expected to serve as a resource for researchers interested in developing vehicle state estimation algorithms for usage in advanced vehicle control and safety systems.

Discrete-Time LPV Observer With Nonlinear Bounded Varying Parameter and Its Application to the Vehicle State Observer

In this paper, we designed a unique structure and developed a novel linear parameter varying (LPV) formulation design for a vehicle-model-based state observer. The design problem for a vehicle state observer was totally reformulated into an LPV system that depended on online accessible time-varying parameters. The proposed LPV formulation, which is presented for the first time in this paper, did not require high computational costs compared to a conventional approach using an extended Kalman filter (EKF) including a nonlinear term. With regard to vehicle state estimation using a global positioning system, we considered the measured course angle as a varying parameter. Observer gain scheduling was determined using an $H_2$ filter based on the linear matrix inequality approach. The state observer gain using a discrete-time $H_2$ filter could minimize the error from a disturbance to a state. The stability of the proposed algorithm is proven, and the performance of the proposed method was demonstrated with an experimental test through a performance comparison with the EKF.

An improved lane departure warning algorithm based on fusion of F-Kalman filter and F-TLC

In order to reduce the lane departure accident caused by driver’s negligence, LDWS (lane departure warning system) has become increasingly popular and important. However, most researches proposed mainly focus on how to detect lane markings. In this paper, we propose an improved lane departure warning algorithm based on fusion of F-Kalman filter (kalman filter based on fuzzy logic) and F-TLC (time to lane crossing based on fuzzy logic). First of all, least square method is used to calculate the distance between vehicle and lane markings. Then the estimation of vehicle states in the future is generated by means of the traditional kalman filter. To better work for lateral offset estimation, a fuzzy model is adopted to change the size of covariance matrices, which is used to adjust the traditional kalman filter in time. Finally, we further put forward to utilize F-TLC to generate multi-grade alarm. Extensive experiments are conducted on different conditions. Experimental results indicate that our warning method works efficiently. The average time consumed for system in each frame is 20.0216 ms. The proposed method possesses good robustness, and can be widely us\ed in LDWS.

Two longitudinal fault tolerant control architectures for an autonomous vehicle

This paper presents two Fault Tolerant schemes for more reliable spacing control of an autonomous vehicle. The nonlinear longitudinal model of the vehicle is described by the Lipschitz representation. Based upon this representation, a state feedback integral controller is designed, as well as, fault estimation observers. This proposal work is focused on Lyapunov theory ensuring H∞ criterion and L2-gain norm in the development of the Linear Matrix Inequality constraints. The design also includes an estimation of vehicle states and an additive fault with the purpose of achieving highly robust and safe vehicle inter-distance control. To carry out this work, experiments are highlighted on prototype vehicle to validate the proposed FTC scheme in different autonomous driving scenarios.