Vehicle State Estimation Research Articles

Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control

Utilizing Vehicle-to-everything (V2X) communication technologies, vehicle platooning systems are expected to realize a new paradigm of cooperative driving with higher levels of traffic safety and efficiency. Connected and Autonomous Vehicles (CAVs) need to have proper awareness of the traffic context. The cooperative platoon’s performance will be influenced by the communication strategy. In particular, time-triggered or event-triggered are of interest here. The expenses related to communication will increase significantly as the number of connected entities increases. Periodic communication can be relaxed to more flexible aperiodic or event-triggered implementations while maintaining desired levels of performance. This paper proposes a predictive model-based and control-aware communication solution for vehicle platoons. The method uses a fully distributed Event-Triggered Communication (ETC) strategy combined with Model-Based Communication (MBC) and aims to minimize communication resource usage while preserving desired closed-loop performance characteristics. In our method, each vehicle runs a remote vehicle state estimator based on the most recently communicated model and the event-driven communication scheme only updates the model when the performance metric error exceeds a certain threshold. Our approach achieves a significant reduction in the average communication rate (82%) while only slightly reducing control performance (e.g., less than 1% speed deviation).

Adaptive vehicle dynamics state estimator for onboard automotive applications and performance analysis

This work describes the development of a vehicle state estimator, designed to be employed onboard in real-time, based on the data acquisitions already available within the vehicle CAN bus infrastructure. The proposed estimation algorithm, taking into account the suspension elasto-kinematics, the longitudinal, lateral and combined dynamics of the tyres as well as the aligning interaction torques at the ground, represents at the current stage a significantly complete tool, since it also provides an estimation of road slope and banking angles, generalising the adoption of the presented vehicle model even on highly inclined roads. A further advantage of the presented estimator consists of its capability to identify in real-time and with a good level of accuracy the tyre/road interaction physical characteristics in terms of grip and cornering stiffness.

Vehicle state and parameter estimation based on adaptive robust unscented particle filter

In order to solve the problem that the measured values of key state parameters such as the lateral velocity and yaw rate of the vehicle are easily interfered by random errors, a filter estimation method of vehicle state is proposed based on the principle of robust filtering and the unscented particle filter algorithm. Based on the establishment of a 3-DOF non-linear dynamic model and the Dugoff tire model of the vehicle, the adaptive robust unscented particle filter(ARUPF) is used to filter and estimate the parameters of the vehicle state, and to realize the longitudinal and lateral speed as well as the yaw rate of the vehicle during the driving process. The simulation and the real vehicle test results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving state estimation can be realized, the measurement parameters can be effectively filtered, and the estimation accuracy is high.

PASE: An autonomous sequential framework for the state estimation of dynamical systems

Kalman filter (KF) and its variants are commonly used in estimating states of dynamical systems. For accurate estimation of states, researchers have developed intelligent KFs by combining them with machine learning algorithms. The inherent variations in the vehicle dynamics and uncertainties in the environment require the development of an autonomous sequential framework for continuous estimation. Further, the need for real-time estimation of states in certain applications requires low computer memory usage and low computational cost while implementing autonomous-structured learning algorithms. In this paper, a parsimonious autonomous sequential estimator (PASE) is proposed, which combines the KF-based estimator and autonomous-structured recurrent parsimonious learning machine (rPALM) in a sequential manner. The rPALM overcomes the dependency on target variables while point-to-hyperplane distance calculation. The performance of PASE has been evaluated extensively by comparing it with various batch-learning algorithms and single-pass learning-based intelligent estimators. The results clearly indicate that PASE provides better estimation accuracy with a compact architecture for both linear and nonlinear dynamical systems. Finally, the performance of PASE has been evaluated with experimental data for the state estimation of an unmanned ground vehicle while the training of the learning machine is performed with the simulated data. The estimation accuracy in such a scenario is justifying its appropriateness in real-world applications.

A novel maximum correntropy adaptive extended Kalman filter for vehicle state estimation under non-Gaussian noise

For vehicle state estimation, conventional Kalman filters work well under Gaussian assumptions. Still, they are likely to degrade dramatically in the practical non-Gaussian situation (especially the noise is heavy-tailed), showing poor accuracy and robustness. This article presents an estimation technique based on the maximum correntropy criterion (MCC) combined with an adaptive extended Kalman filter (AEKF), and an extended Kalman filter (EKF) based on the MCC has also been studied. A lateral-longitudinal coupled vehicle model is developed, while an observer containing the state vectors such as yaw rate, sideslip angle, vehicle velocity and tire cornering stiffness is designed using easily available in-vehicle sensors and low-cost GPS. After analyzing the algorithmic complexity, the proposed algorithm is validated by sine steering input and double lane change driving scenarios. Finally, it is found that MCC combined with AEKF/EKF has stronger robustness and better estimation accuracy than AEKF/EKF in dealing with non-Gaussian noise for vehicle state estimation.

Vehicle Teleoperation: Human in the Loop Performance Comparison of Smith Predictor with Novel Successive Reference-Pose Tracking Approach

Vehicle teleoperation has the ability to bridge the gap between completely automated driving and manual driving by remotely monitoring and operating autonomous vehicles when their automation fails. Among many challenges related to vehicle teleoperation, the considered ones in this work are variable time delay, saturation of actuators installed in vehicle, and environmental disturbance, which together limit the teleoperation performance. State-of-the-art predictive techniques estimate vehicle states to compensate for the delays, but the predictive states do not account for sudden disturbances that the vehicle observes, which makes the human-picked steer inadequate. This inadequacy of steer deteriorates the path-tracking performance of vehicle teleoperation. In the proposed successive reference-pose-tracking (SRPT) approach, instead of transmitting steering commands, the reference trajectory, in the form of successive reference poses, is transmitted to the vehicle. This paper introduces a method of generation of successive reference poses with a joystick steering wheel and compares the human-in-loop path-tracking performance of the Smith predictor and SRPT approach. Human-in-loop experiments (with 18 different drivers) are conducted using a simulation environment that consists of the integration of a real-time 14-DOF Simulink vehicle model and Unity game engine in the presence of bidirectional variable delays. Scenarios for performance comparison are low adhesion ground, strong lateral wind, tight corners, and sudden obstacle avoidance. Result shows significant improvement in reference tracking and in reducing human effort in all scenarios using the SRPT approach.

Secure Distributed Adaptive Platooning Control of Automated Vehicles Over Vehicular Ad-Hoc Networks Under Denial-of-Service Attacks.

This article deals with the problem of secure distributed adaptive platooning control of automated vehicles over vehicular ad-hoc networks (VANETs) in the presence of intermittent denial-of-service (DoS) attacks. The platoon, which is wirelessly connected via directed vehicle-to-vehicle (V2V) communication, is composed of a group of following vehicles subject to unknown heterogeneous nonlinearities and external disturbance inputs, and a leading vehicle subject to unknown nonlinearity and external disturbance as well as an unknown control input. Under such a platoon setting, this article aims to accomplish secure distributed platoon formation tracking with the desired longitudinal spacing and the same velocities and accelerations guided by the leader regardless of the simultaneous presence of nonlinearities, uncertainties, and DoS attacks. First, a new logical data packet processor is developed on each vehicle to identify the intermittent DoS attacks via verifying the time-stamps of the received data packets. Then, a scalable distributed neural-network-based adaptive control design approach is proposed to achieve secure platooning control. It is proved that under the established design procedure, the vehicle state estimation errors and platoon tracking errors can be regulated to reside in small neighborhoods around zero. Finally, comparative simulation studies are provided to substantiate the effectiveness and merits of the proposed control design approach on maintaining the desired platooning performance and attack tolerance.

An Active Perception Framework for Autonomous Underwater Vehicle Navigation Under Sensor Constraints

Inertial navigation for autonomous underwater vehicles (AUVs) is challenging because of the drift error caused by the noise and measurement errors of inertial sensors, typically packaged as an inertial measurement unit (IMU), integrated over time. To mitigate the drift error, recent AUV state estimation approaches incorporate external references or environmental information obtained from exteroceptive sensors, with increased costs and limited operational domains. For improved navigation under sensor constraints, this article proposes an active perception framework that exploits vehicle motion to estimate the flow state together with the vehicle state using IMU and depth sensors only. The proposed framework uses the estimated flow state as external information to improve vehicle state estimation. We construct a linear time-varying system for the flow state, separated from a nonlinear system for the vehicle state. This formulation allows us to analyze uniform complete observability for the flow state, which is found to depend on vehicle motion. Then, along with vehicle and flow state estimators, we design a vehicle controller to enable vehicle motion to maximize an information metric pertaining to estimation performance based on either observability or constructability Gramian for the flow state. The proposed framework is validated through simulations for a case study with a vehicle descending through the water column in a time-varying flow field. The effectiveness of the framework is demonstrated by comparing results obtained from its four implementations with those from baseline approaches without active perception.

Hierarchical estimation of vehicle state and tire forces for distributed in-wheel motor drive electric vehicle without previously established tire model

In this paper, a hierarchical estimator combined with the nonlinear observer and particle filter (PF) is proposed to accurately estimate the vehicle state and tire forces of distributed in-wheel motor drive electric vehicles (DIMDEVs) when the traditional tire models are not available. The proposed estimator consists of lower and upper layers. The lower layer, i.e. longitudinal tire force nonlinear observer (LTFNO) aims at estimating the longitudinal force based on the available drive/brake torques and rotational speed of wheels. The convergence of LTFNO is proved by the invariant set principle. The upper layer receives these estimated longitudinal tire forces from LTFNO and estimates the vehicle state including lateral tire forces based on an expert model (EM). The designed EM utilizes basic knowledge and rules about tire characteristics to approximate the unknown lateral tire force. The upper estimator combines with EM (EEM) to further improve the accuracy. The EEM takes the modeling errors and disturbances into account and avoids the usage of complex established tire models. Then PF is applied in the upper layer to complete the estimation, which only needs measurable longitudinal/lateral accelerations and yaw rate signals. Finally, the effectiveness of the designed hierarchical estimator is verified by Carsim and Simulink co-simulations. The results show the proposed strategy can accurately estimate the vehicle state and tire forces in real-time.

An extended LuGre-brush tyre model for large camber angles and turning speeds

ABSTRACT This paper presents a novel tyre model which combines the LuGre formulation with the exact brush theory recently developed by the authors, and which accounts for large camber angles and turning speeds. Closed-form solutions for the frictional state at the tyre-road interface are provided for the case of constant slip inputs, considering rectangular and elliptical contact patches. The steady-state tyre characteristics resulting from the proposed approach are compared to those obtained by employing the standard formulation of the LuGre-brush tyre models and the exact brush theory for large camber angles. Then, to cope with the general situation of time-varying slips and spins, two approximated lumped models are developed that describe the aggregate dynamics of the tyre forces and moment. In particular, it is found that the transient evolution of the tangential forces may be approximated by a system of two coupled ordinary differential equations (ODEs), whilst the dynamics of the self-aligning moment may be described by combining two systems of two coupled ODEs. Given its stability properties and ease of implementation, the lumped one may be effectively employed for vehicle state estimation and control purposes.

A Robust Unscented M-Estimation-Based Filter for Vehicle State Estimation With Unknown Input

Longitudinal velocity, lateral velocity, and front wheel steering angle are crucial states for vehicle active safety features. However, direct measurement of these variables requires expensive measurement instruments and can be easily affected by vehicle nonlinear dynamics, outliers and noise pollution. Moreover, unknown inputs bring great challenges to their accurate estimation. Therefore, this paper develops a novel robust unscented M-estimation-based filter (RUMF) for vehicle state estimation with unknown driver steering torque. The nonlinear system model is constructed based on the vehicle dynamics model. Unscented transformation (UT) and statistical linearization are implemented to transform the nonlinear process and measurement function into a linear-like regression form with data redundancy to achieve outlier suppression. Then, an M-estimation-based iterated algorithm is designed to address process uncertainty and innovation and observation outliers for robust vehicle state estimation. The iteratively reweighted least-squares (IRLS) method based on the M-estimation methodology is utilized for unknown input estimation. Simulations and experimental results compared with extended Kalman filter (EKF) and particle filter (PF) have verified the effectiveness and robustness of the proposed algorithm.

Vehicle state estimation based on extended Kalman filter and radial basis function neural networks

To improve the reliability of vehicle state parameter estimation, a vehicle state fusion estimation method based on dichotomy is proposed. An extended Kalman filter algorithm is designed based on the vehicle 3 degrees of freedom dynamic model. Meanwhile, considering the influence of dynamic model and sensor noise and its coefficient selection on the estimation results, a radial basis function neural network estimation algorithm is designed. To further improve the reliability of the estimation algorithm, a method of estimation algorithm fusion is proposed based on the idea of mutual compensation between model- and data-driven estimation algorithms. The weights of the estimation results of different algorithms are assigned through the dichotomy. The redundancy and fusion of estimation algorithms can improve estimation performance. The effectiveness of the fusion method is verified by the co-simulation of MATLAB/Simulink and CarSim, and the real vehicle test. The results show that the change trend of the estimation result is consistent with the actual state parameters change trend, and the estimation accuracy after algorithm fusion is significantly improved compared to a single extended Kalman filter or radial basis function.

Autonomous Vehicle State Estimation and Mapping Using Takagi-Sugeno Modeling Approach.

This paper proposes an optimal approach for state estimation based on the Takagi–Sugeno (TS) Kalman filter using measurement sensors and rough pose obtained from LIDAR scan end-points matching. To obtain stable and optimal TS Kalman gain for estimator design, a linear matrix inequality (LMI) is optimized which is constructed from Lyapunov stability criteria and dual linear quadratic regulator (LQR). The technique utilizes a Takagi–Sugeno (TS) representation of the system, which allows modeling the complex nonlinear dynamics in such a way that linearization is not required for the estimator or controller design. In addition, the TS fuzzy representation is exploited to obtain a real-time Kalman gain, avoiding the expensive optimization of LMIs at every step. The estimation schema is integrated with a nonlinear model-predictive control (NMPC) that is in charge of controlling the vehicle. For the demonstration, the approach is tested in the simulation, and for practical validity, a small-scale autonomous car is used.

Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter

Aiming at solving problem of vehicle state estimation, an adaptive fading unscented Kalman filter(AFUKF) algorithm was proposed. Based on this purpose, a 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established firstly. Then, the vehicle state estimator based on Kalman filter was designed to solve the problem of vehicle state estimation. The simulation verification shows the effectiveness and reliability of the designed estimator for vehicle state estimation. Compared with other traditional methods, the calculation accuracy is higher for the AFUKF algorithm to solve the problem of vehicle state estimation. The study can help drivers easily identify key state estimation in safe driving area.

Longitudinal-lateral-cooperative estimation algorithm for vehicle dynamics states based on adaptive-square-root-cubature-Kalman-filter and similarity-principle

It is infeasible to measure vehicle dynamics states (VDS) directly without expensive measurement instruments, especially for the tire-road peak adhesion coefficient (μmax). However, four-wheel-independent-drive-electric-vehicle (4WIDEV) provides convenience for the observation of these dynamic states, because the rotation rate and torque of the in-wheel motor can be acquired directly. Vehicle nonlinear longitudinal-lateral dynamics, the single estimation method for all VDS and the time-varying measurement noise of sensors cause difficulties for the observation. Common the extended-Kalman-filter (EKF) is unsuitable to estimate VDS in strong nonlinear region. This paper propose a longitudinal-lateral cooperative estimation algorithm based on adaptive-square-root-cubature-Kalman-filter (ASRCKF) and partitioned similarity-principle (SP) to estimate the vehicle states and the tire-road peak adhesion coefficient sequentially for 4WIDEV. Firstly, a nonlinear 7-degree-of-freedom (7DOF) vehicle model and magic-formula (MF) tire model are built as the base of the successive estimation scheme. Then, recursive-least-squares (RLS) is adopted to estimate the tire longitudinal force. With the estimated tire longitudinal force, an ASRCKF which can be adjusted adaptively by the feedback dynamics states, is designed for the estimation of vehicle states. Next, the SP algorithm combined with the characteristic of longitudinal-lateral dynamics, which is benefit for μmax estimation when tire dynamics enters the nonlinear region, is proposed. Finally, experiment and simulation results show that excellent performance can be achieved with the proposed estimation method in varying driving conditions.

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.

Scalable and Resilient Platooning Control of Cooperative Automated Vehicles

This paper addresses the problem of distributed cooperative longitudinal control of automated vehicle platoons subject to a variety of uncertainties, including unknown engine time lags, external disturbances, measurement noises, and actuator anomaly in follower vehicles as well as unknown leader control. First, a unified framework is proposed for accomplishing resilient vehicle platooning, which empowers longitudinal vehicle state estimation, anomaly signal estimation and compensation, and adaptive platoon controller design to be addressed in a comprehensive way. Second, a novel scalable platooning control design approach is developed to guarantee desired platoon stability and resilience over generic communication topologies and various spacing policies. A salient feature of the approach is that the design procedure does not depend on any global information of the associated topology, and thus preserves essential scalability for large and/or size-varying platoons. Third, it is shown that the proposed longitudinal platooning control approach is promising for performing flexible cooperative maneuvers such as platoon splitting and merging that are beyond the capacity of most existing longitudinal platooning strategies. Finally, simulation results for different platoon maneuvers are elaborated to substantiate the efficacy of the proposed approach.

Innovative Interaction Approach in IMM Filtering for Vehicle Motion Models With Unequal States Dimension

Robust and adaptive vehicle state estimation and tracking algorithms have become a very important part within the autonomous driving field. The family of interacting multiple model (IMM) filters has shown to provide very effective and accurate state estimation in systems whose behavior patterns change significantly over time. The main reason for the improved performance of IMM filters compared to single model approaches is the mode mixing, which constantly aligns the different models. This paper proposes an innovative way for the mode mixing, when the state-vectors of the models are of different size. The proposed mixing approach consists of two parts: firstly mixing the common states and secondly weighting between original and mixed states based on the model probabilities. Results from artificial simulations and real world measurement setups are shown to demonstrate the validity of the approach. Compared to previously suggested solutions, the proposed approach is more general and the overall complexity of the mode mixing step is reduced, which is the main contribution of the presented paper.

Estimation algorithm for vehicle state estimation using ant lion optimization algorithm

In order to solve the problem of vehicle state estimation, an unscented Kalman filter state parameter estimator based on the Ant lion algorithm is proposed. Aiming at the uncertainty of the noise covariance matrix in the unscented Kalman filter (UKF) process, the Ant lion optimization algorithm (ALO) is used to optimize it. Based on the purpose, a 3-DOF nonlinear vehicle estimation model with Magic formula tire model was established firstly. Then the slalom road operating condition was simulated. The simulation results show that the estimated values of the key state variables are in better agreement with the virtual test values indicating the proposed algorithm having a good estimation performance. And also, compared with the estimation results of the UKF algorithm, the maximum error and the root mean square error of the estimation algorithm proposed in this paper are both smaller than the estimated value of the UKF algorithm. The results of a real-vehicle experiment demonstrate that the proposed method can be used effectively and accurately for solving the vehicle-state estimation problem. The study can provide precise status information for vehicle stability control under extreme conditions.

Mass estimation of ground vehicles based on longitudinal dynamics using loosely coupled integrated navigation system and CAN-bus data with model parameter estimation

This work considers real-time estimation of the mass of ground vehicles equipped with on-board diagnostics (OBD-II) capabilities. The mass is estimated with a recursive least squares filter, based on vehicle longitudinal dynamics using data from a 6-axis inertial measurement unit (IMU), OBD-II data, as well as global navigation satellite system (GNSS) positions. Only basic OBD-II parameters from the controller area network (CAN-bus) are used, including vehicle speed, engine speed, and engine load. This ensures a broad applicability of the presented work. A loosely coupled fusion of IMU data, OBD-II vehicle speed, and GNSS positions is used for vehicle state estimation. The road grade is estimated from the vehicle pitch angle, current gear is predicted from the ratio of engine and vehicle speeds, and engine torque is estimated from a linear regression with engine speed and load as parameters. The regression is carried out using engine torque estimated from the vehicle longitudinal dynamics, assuming a known vehicle mass. Model parameters are estimated from the engine torque regression, utilizing a linear correlation between the engine torque and OBD-II engine load. A method for automatic estimation of vehicle gearing ratio values from drive data is also proposed. The result is a two-phase approach with an initial training phase for parameter estimation, followed by an operational phase where the vehicle mass can be estimated. The methods are validated using data from a modern car. Parameters and regression coefficients are first estimated from a single test drive. Vehicle mass is then estimated using data from 18 drives on an 85 km test route, comprising of more than 1600 km of driving under different condition, including varying vehicle loads, tire pressures, and window openings. Using the fitted model parameters, the model is generally able to estimate masses within ± 5% of the actual. The change in tire pressures and windows openings do not show significant effects on the estimated masses. Ambient wind during the test drives appears to present a significant source of uncertainty in the estimates at higher speeds. A method for compensation of the ambient wind should be investigated.