Vehicle Sideslip Angle Estimation Research Articles

Robust joint estimation of sideslip angle and tire lateral force for distributed drive electric vehicle

Accurate estimation of vehicle sideslip angle (SA) and tire lateral force (TLF) is essential for the effective functioning of vehicle active safety control systems. However, effective estimation of SA and TLF using low-cost on-board sensors is a major challenge. In this paper, a robust joint estimation method of SA and TLF for distributed drive electric vehicle (DDEV) is proposed. Adaptive sliding-mode observer (ASMO) is used to estimate the TLF and is used as the input to the subsequent filter. Maximum correntropy criterion (MCC) is introduced to improve the robustness of the unscented Kalman filter (UKF) under mixed Gaussian noise, and the maximum correntropy unscented Kalman filter (MCUKF) algorithm is composed to estimate SA. The effectiveness of the proposed ASMO and MCUKF joint estimation method is verified by Simulink/Carsim joint simulation experiments. The experimental results show that compared with the existing methods, the estimation method proposed in this paper effectively improves the estimation accuracy and robustness of SA and TLF under the mixed Gaussian environment, which is of great significance for the improvement of vehicle active safety.

RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect, a vehicle sideslip angle estimation method adopting a robust bias compensation Kalman filter and adaptive weight fusion strategy is proposed. On the basis of the extended Kalman filter algorithm, and with the goals of estimation exactitude and robustness, considering the potential signal deviation, a vehicle state robust deviation compensation Kalman filter estimation algorithm considering bias compensation and residual covariance matrix weighting is proposed. Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.

Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

Vehicle Sideslip Angle estimation under critical road conditions via nonlinear Kalman filter-based state-dependent Interacting Multiple Model approach

The knowledge of Vehicle Sideslip Angle (VSA) can play an essential role in active safety vehicle control systems. However, due to the high costs of sensing instruments, this information is difficult to be directly measured onboard of series production vehicles, restricting de facto its application in practice. It follows that there is a need for online VSA estimation methods only based on available measurements from low-cost sensors. From this perspective, this work proposes a strategy based on Interacting Multiple Model (IMM) filters, which does not require tyre–road friction coefficient knowledge. By integrating the available onboard sensor data, the IMM estimates relevant information in different driving conditions leveraging a 2-Degrees Of Freedom (DOF) single-track vehicle model embedding a Dugoff tyre representation. Two alternative IMM algorithms based on the Extended (EKF) and Unscented Kalman filter (UKF) are developed. Moreover, while usually the transition probabilities among models in classical IMMs are fixed and set on prior information and/or dedicated analysis, here these conservative hypotheses are relaxed introducing a state-dependent Markov transition matrix based on a novel model switching algorithm. The effectiveness of the new proposed methods is evaluated on extensive non-trivial simulation scenarios through a Monte Carlo analysis exploiting an accurate 15-DOF vehicle model via a purposely designed high-fidelity co-simulation platform embedding the dSPACE software Automotive Simulation Model (ASM). Results provide a meaningful comparative performance analysis between the IMMEKF and IMMUKF solutions, as well as with respect to traditional IMM based on constant probabilities transition matrix, blue in both the EKF and UKF configuration. Finally, the developed IMM-based estimation strategy is tested in two realistic driving scenarios to assess the VSA estimation accuracy in case of abrupt changes in road surface conditions.

Review on vehicle sideslip angle estimation

Review on vehicle sideslip angle estimation

Review on vehicle sideslip angle estimation

Review on vehicle sideslip angle estimation

A Hybrid Model for Vehicle Sideslip Angle Estimation Based on Attention Regression

A Hybrid Model for Vehicle Sideslip Angle Estimation Based on Attention Regression

Reliable Estimation of Automotive States Based on Optimized Neural Networks and Moving Horizon Estimator

Accurate estimation of vehicle sideslip angle and attitude angles are essential for the safety control and lateral behaviour of driving performance. In this article, the variation of wheels cornering stiffness is considered for sideslip estimation and addressed by introducing a recursive least squares approach. Based on the nonlinear vehicle dynamic model and the investigated coupling effect between lateral and longitudinal velocity, an optimized moving horizon estimator is proposed to obtain the vehicle sideslip angle, in which an iteration decent algorithm is integrated. Furthermore, a framework, consisting of inertial navigation system measurements, a dual neural network and a square-root cubature Kalman filter, is designed, such that the influence of sensor noise and varied maneuvers are alleviated when estimating the system states. Finally, extensive simulation and field experiments are carried out on different driving scenarios to verify the effectiveness of the developed method. The obtained results clearly indicate the satisfactory estimation accuracy of the designed strategy, superior to the existing estimation methods, such as sole neural networks methods and Kalman-based filters.

Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque

The sideslip angle is crucial for the lateral stability state and stability control of intelligent commercial vehicles. However, sensors that can be used for direct measurements are often complex, expensive, and difficult to install in commercial vehicles. To estimate the vehicle sideslip angle, a state observer derived from the extended Kalman filter (EKF) method is proposed, and the state observer is estimated based on steering torque rather than steering angle. The transfer functions between the sideslip angle–steering torque and sideslip angle–steering angle are established, respectively, and the analysis shows that the steering torque signal has a more rapid and more direct reaction due to the hydraulic pressure in the steering system. Finally, the proposed method is validated using Simulink/TruckSim simulation hardware-in-the-loop bench test, and the results show that the proposed method can accurately reflect the actual state of the sideslip angle with good reliability and effectiveness.

Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm.

This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach.

Autonomous Vehicle Kinematics and Dynamics Synthesis for Sideslip Angle Estimation Based on Consensus Kalman Filter

An autonomous vehicle sideslip angle estimation algorithm is proposed based on consensus and vehicle kinematics/ dynamics synthesis. Based on the velocity error measurements between the reduced Inertial Navigation System (R-INS) and the global navigation satellite system (GNSS), a velocity-based Kalman filter is formalized to estimate the velocity errors, attitude errors, and gyro bias errors of the R-INS. The observability issue of the heading error, which affects sideslip estimation, is analyzed. Then, to enhance the observability and improve the estimation accuracy of the heading error under normal driving conditions, a consensus Kalman information filter is developed to synthesize the vehicle kinematics and dynamics and estimate the heading error. Within the developed consensus framework, one node augments a novel heading error measurement from a linear vehicle-dynamic-based sideslip estimator and another node adopts the heading error from the GNSS course. Next, based on the vehicle lateral excitation level, a weighting scheme is proposed to fuse the error state estimates from the velocity-based and consensus Kalman state observers. The stability of the proposed state observers is also investigated. Comprehensive experimental studies, including critical slalom, slight/normal double lane change, and normal driving maneuvers, were conducted to verify the proposed estimation framework; they confirm the reliability and accuracy of the estimator in various automated driving conditions even in comparison with state-of-the-art methods that utilize more measurements (dual-antenna GNSS). Also, this novel multisensor framework is extendable to leverage speed information from other sensors such as cameras and light detection and ranging (LiDAR) to increase reliability and accuracy.

Event-Triggered Vehicle Sideslip Angle Estimation Based on Low-Cost Sensors

Accurate vehicle sideslip angle estimation is crucial for vehicle stability control. In this article, an enabling event-triggered sideslip angle estimator is proposed by using the kinematic information from a low-cost global positioning system (GPS) and an on-board inertial measurement unit (IMU). First, a preliminary vehicle sideslip angle is derived using the heading angle of GPS and the yaw rate of IMU, and an event-triggered mechanism is proposed to eliminate the accumulative estimation error. The algorithm convergence is guaranteed through theoretical deduction. Second, a longitudinal and a lateral vehicle velocity are obtained using the preliminary vehicle sideslip angle and the measured GPS velocity and their kinematic relationship, based on which a multisensor fusion and a multistep Kalman filter scheme are, respectively, presented to realize longitudinal and lateral vehicle velocity estimation. By doing this, the update frequency and estimation accuracy of the vehicle sideslip angle estimate can be further improved to meet the requirement of online implementation. Finally, the effectiveness and reliability of the proposed scheme are verified under comprehensive driving conditions through both hardware-in-loop (HIL) and field tests. The results show that the proposed event-triggered sideslip angle estimator has a mean estimation error of 0.029 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> and of 0.14 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> in the HIL and field tests, exhibiting better estimation accuracy, reliability, and real-time performance compared with other typical estimators.

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.

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

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

Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic

Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.

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

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

Event-Triggered Asynchronous Fuzzy Filtering for Vehicle Sideslip Angle Estimation With Data Quantization and Dropouts

This article investigates the event-triggered fuzzy filtering issue for vehicle sideslip angle estimation with consideration of data quantization and dropouts. First, an uncertain Takagi–Sugeno fuzzy model is developed to describe vehicle nonlinear dynamics resulted from nonlinear tire dynamics, varying velocity, uncertain mass, and yaw moment inertia. Then, an adaptive event-triggered scheme is introduced between the sensor and the filter for the decision of releasing sampled data to economize limited network resource. Moreover, the network-induced constraints, such as delay, data quantization, and dropouts, are taken into account to improve the robustness of the filtering method. Based on the Lyapunov stability theory, a new event-triggered asynchronous fuzzy filtering method is proposed by establishing an augmented Lyapunov–Krasovskii functional candidate and applying integral inequalities in the derivation. Finally, simulation results are presented to verify the advantages of the proposed method in comparison with the existing results.

Experimental Comparison of The Two Most Used Vehicle Sideslip Angle Estimation Methods for Model-Based Design Approach

Vehicle sideslip angle estimation is still one of the most challenging research topics in the automotive industry. Many papers can be found on this topic, where authors propose varied methods to reach the goal. Which is the most effective? After an extensive literature review, two very different methods have been identified as the most used: Extended Kalman Filter with dynamic model and Artificial Neural Network. In this work a comparison among these methods is presented. A fully instrumented car has been used to gather typical vehicle dynamics data and feed the models required for a model-based design approach. Results showed that each method has either positive aspects or drawbacks.

A Novel PWA Lateral Dynamics Modeling Method and Switched T-S Observer Design for Vehicle Sideslip Angle Estimation

In this article, a novel vehicle sideslip angle estimation method is proposed using the Takagi–Sugeno (T–S) observer based on a piecewise affine (PWA) lateral dynamics model. Since conventional lateral dynamics models have complex structures and nonlinearities, difficulties in sideslip angle observer design and online implementation are inevitable. To address nonlinearities, the PWA and T–S fuzzy modeling approaches are applied in this article. First, a nominal PWA model is obtained using the nominal tyre load and tyre-road friction coefficient. An explicit relationship between the PWA model parameters and the tyre load, tyre-road friction coefficient is deduced based on the Dugoff tyre model. Then, a novel PWA lateral dynamics model is obtained considering a varying tyre load and tyre-road friction coefficient. Due to the residual nonlinear terms, this PWA model is further converted into a T–S fuzzy model. Finally, a switched T–S observer is designed as it is computationally simple and efficient enough for online estimation. To validate the effectiveness of the proposed approach, experiments are conducted on both low- and high-friction coefficient roads. Experimental results show that the proposed PWA modeling technique is effective and the T–S observer can estimate the sideslip angle well under both stable and unstable steering maneuvers.

Vehicle sideslip angle estimation: fusion of vehicle kinematics and dynamics

Vehicle sideslip angle estimation: fusion of vehicle kinematics and dynamics