车辆质心位置的离线辨识

The position of the center of gravity (CG) of a vehicle is an important parameter as it significantly affects vehicle loads distribution and vehicle dynamics. This article proposes an approach to estimate the longitudinal position of the CG from inertial measurements. The proposed approach does not require prior knowledge of vehicle parameters apart from vehicle wheelbase. The estimation procedure combines a direct estimation from the equations of motion with the state variable filter method used in the identification of continuous time model from sampled data. The filter is chosen based on general knowledge of vehicle dynamics such as the order of magnitude of characteristic frequencies of pitch motion. The estimates are obtained through a least squares method with instrumental variables. The algorithm can be implemented in both offline batch and online recursive forms, with low computational cost suitable for embedded applications. The method is tested and validated on a high-fidelity road vehicle simulation, with good results for different load distributions.

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The accurate estimation of the mass and center of gravity (CG) position is key to vehicle dynamics modeling. The perturbation of key parameters in vehicle dynamics models can result in a reduction of accurate vehicle control and may even cause serious traffic accidents. A dual robust embedded cubature Kalman filter (RECKF) algorithm, which takes into account unknown measurement noise, is proposed for the joint estimation of mass and CG position. First, the mass parameters are identified based on directly obtained longitudinal forces in the distributed drive electric vehicle tires using the whole vehicle longitudinal dynamics model and the RECKF. Then, the CG is estimated with the RECKF using the mass estimation results and the vertical vehicle model. Finally, different virtual tests show that, compared with the cubature Kalman algorithm, the RECKF reduces the root mean square error of mass and CG by at least 7.4%, and 2.9%, respectively.

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Influenced by tire effective radius change, suspension rearrangement, and pitch/roll disturbance due to tire blowout, the vehicle center of gravity (CG) can significantly relocate toward the blown-out tire position. This paper proposes an estimation method of the CG relocation for ground vehicles with tire blowout by utilizing vertical force variations and geometric relationships in tire blowout events. Based on a new recursive least square (RLS) formulation in this paper, the three-dimensional (3D) CG relocation (i.e., the height, the longitudinal and lateral positions) can be estimated simultaneously. Matlab/Simulink and CarSim® co-simulation results for different tire blowout locations validate that the proposed estimation method can effectively and accurately capture the vehicle 3D CG relocation after tire blowout.

Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic®. The use of both Trucksim® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article.

The robust estimation of heavy-duty vehicles (HDVs) sideslip angle is crucial for vehicle stability controls. This paper aims to address the challenge of estimating the sideslip angle for HDVs when carrying cargo. We propose an innovative framework for estimating the sideslip angle of HDVs after loading. To address the problem of deviation in the inertial measurement unit (IMU) output acceleration signal during vehicle turning caused by the change of center of gravity (C.G.) position after loading cargo, we amalgamate the minimum model error (MME) criterion with the cubature Kalman filter (CKF) to accurately estimate the longitudinal C.G. position and carry out the IMU sensor position correction. On this basis, concerning the estimation of the sideslip angle for HDV when carrying cargo, a series-connected structure estimator is proposed, comprising a kinematic model estimator, a tire cornering stiffness estimator, and a dynamics model estimator. In this structure, the kinematic and dynamics model estimator employs CKF. Considering the characteristics of both kinematic and dynamics models, alongside HDVs' variable payload, an innovative adaptive weighted recursive least squares (AWRLS) method is designed for the adaptive estimation of tire cornering stiffness. This allows the series-connected structure estimator to dynamically adjust in real-time, achieving an accurate and robust estimation of the sideslip angle. Finally, through extensive simulations and real-world vehicle experiments, we validate the efficacy of the proposed framework. The results of the experiments show that the proposed method has a satisfactory level of estimation accuracy, significantly outperforming the estimation results of individual models.

Horizontal curves are often associated with roadway crashes due to speed misjudgment and loss of control. With the growing adoption of autonomous and connected vehicles, the accurate estimation of safe speed at curves is becoming increasingly important. The widely used AASHTO design method for safe curve speed estimation relies on an analytical equation based on a simplified point mass model, which often uses conservative parameters to account for vehicular and environmental variations. This paper presents a digital twin-based framework for estimating safe speed at curves using a physics-driven virtual environment developed in the Unity engine. In this framework, a real-world horizontal road curve is selected, and vehicle speed data are collected using a radar gun under various weather conditions. A 3D model of the road curve is constructed in a Unity environment using roadway geometric and elevation data. A parameterized vehicle model is integrated, allowing for variations in mass, acceleration, and center of gravity to reflect different vehicle types and loading scenarios. This simulation identifies the maximum safe speed at which a vehicle can traverse the given curve, providing a more vehicle and environment-specific estimate of the safe operating speed. The study validated that the safe curve speed estimates generated by the simulation were consistent with the real-world speed values observed at a curve. This study demonstrates how a physics-based digital twin can estimate a safer and more adaptive operating speed for vehicles traversing horizontal curves.

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This letter presents a practical method for parameter estimation of a full-size industrial car-like tractor. With only on-vehicle sensors including three encoders, an IMU and a 3D LiDAR, we estimate the effective radius of rear wheels, ratio between the steering wheel angle and road wheel angle, the longitudinal position of center of gravity (COG), the yaw moment of inertia and the longitudinal and cornering tire stiffnesses. This letter innovatively introduces nonlinear vehicle-dynamics constraints into a factor-graph estimation framework that also fuses IMU and LiDAR measurements, thus achieving an easy-to-tune and highly-accurate parameter estimation solution. The batch maximum a posterior (MAP) problem is formulated and solved efficiently for both states and parameters. To make it even easier to tune, we perform specifically-designed motion patterns to simplify the required description model, and we estimate as fewer parameters as possible for one motion pattern. The dynamic constraints are also formulated according to the motion properties. Performance and results of the proposed method are validated in detail with experiments on the industrial car-like tractor.

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Autonomous vehicle requires a high-precision lateral dynamics model for path following and lateral stability control. However, existing physical models suffer from low accuracy due to modeling simplification and inaccurate model parameters, while data-driven models lack physical interpretability and robustness. To address these issues, a hybrid architecture inspired by vehicle lateral dynamics is developed in this study, which embeds the data-driven model into a physical model for parameter identification and error characterization to achieve accurate and interpretable modeling. Specifically, a physical lateral dynamics model with error analysis is established at first, and the problems of modeling error characterization and parameter identification are formulated. Then, the physical lateral dynamics model is deformed, and the modeling errors and cornering stiffness are unified into compound parameters. Using this deformed physical model, the modeling errors can be characterized by the identification of these compound parameters. To obtain high-precision compound parameters, a neural network-based parameter identification method is proposed, and the identified time-varying parameters enable high-precision characterization of modeling errors and parameters using data knowledge. By embedding the neural network into the deformed physical model, a hybrid model integrating physical laws and data knowledge is finally established for the description of vehicle lateral dynamics. Simulation and experimental results demonstrate that the proposed hybrid model realizes more accurate modeling of vehicle lateral dynamics than conventional physical and data-driven models.

In this study, we present a robust framework for vehicle tire cornering stiffness identification, leveraging commonly available lateral acceleration and yaw rate measurements. In contrast to most of the state-of-the-art approaches, our methodology does not rely on (augmented) state estimation, but on batch least-squares optimisation of larger time windows. This batch-approach has as a major benefit that many of the observability issues which are encountered in estimation-based methods are completely eliminated. By immediately exploiting the measurement equations, rather than comparing dynamic simulation responses, we obtain a very efficient and sparse formulation which enables online processing. The methodology was validated on (openly available) datasets from two different vehicles – a Ferrari 250LM and Range Rover Evoque, delivering consistent and accurate results for both vehicles. This work shows that batch optimisation can be a very promising alternative for the more common state-estimation approaches for extracting reliable vehicle cornering stiffnesses in various driving scenarios with a much more straightforward tuning.

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We present a neural network based Takagi-Sugeno (TS) fuzzy observer to estimate the lateral speed (or sideslip angle) of nonlinear vehicle dynamics subject to modeling uncertainties and unknown inputs. To this end, we first propose a TS fuzzy reduced-order observer design, which can be implemented with low computation effort, for nonlinear systems. The stability and robustness of the observer scheme against the modeling uncertainty is guaranteed by the $\mathscr {H}_{\infty }$ filtering method. A data-driven approach is proposed to construct feedforward neural networks (NNs) for uncertainty approximation. This data-driven approach exploits a specific sliding mode observer (SMO) to identify the model uncertainty data from the collected training data. The NN-based uncertainty approximation is incorporated into the TS fuzzy observer structure to mitigate the effect of uncertainty and improve the estimation quality. Via Lyapunov's stability theory, design conditions of both the TS fuzzy reduced-order observer for dynamics estimation and the SMO for uncertainty identification are derived in terms of linear matrix inequalities. Experimental results obtained with the INSA autonomous vehicle on a real test track demonstrate the effectiveness of the proposed TS fuzzy observer under various driving scenarios. Performance comparisons are also performed to illustrate the interest of using NN-based uncertainty approximation for the new reduced-order observer scheme, especially under extreme driving conditions.

In order to improve the identification effect of vehicle key performance parameters, this paper applies the PSO algorithm to the identification of vehicle key performance parameters and focuses on analyzing the parameters of the vehicle key performance parameter monitoring equipment and the propagation environment. The improvement of sensitivity of the monitoring receiver can effectively extend the coverage distance. NLOS is a ubiquitous transmission environment and changing NLOS to LOS propagation further extends the coverage distance. In order to ensure the communication quality of radio communication users, ensure that the service area covered by radio waves and the reliability of radio wave propagation, this paper calculates the propagation loss from the receiving antenna to the transmitting antenna in detail. The experimental results show that the identification method of vehicle key performance parameters based on PSO algorithm proposed in this paper can play an important role in the identification of vehicle key performance parameters.

The center of gravity (CG) of a vehicle is a key parameter that helps determine vehicle stability, braking efficiency, and safety. In a gravity vehicle, the mass of the vehicle is also an important factor in vehicle performance because it provides the sole force of propulsion. We hypothesized that if a vehicle was constructed according to mathematically-derived optimal mass and CG location, then a fast and accurate vehicle would result. To test this hypothesis, we constructed a gravity vehicle, which is a vehicle powered by its own gravity on a ramp. Mathematical calculations were used to rationalize this hypothesis. Shifting the CG rearward increased the vehicle’s effective launching height on the ramp and corresponding gravitational potential energy, resulting in greater kinetic energy and speed. However, the accuracy (m-1), defined as the reciprocal of braking distance from the target, increased initially, peaked, and then decreased as the vehicle mass increased. We performed experiments with five mass parameters and three load locations, using an unloaded vehicle as control. Speed and accuracy were then measured for 16 sets of data. Compared to front and centrally-loaded vehicles, the rear-loaded vehicles displayed the best results. As the mass increased to a medium value, both the speed and accuracy reached a maximum. The experimental results supported the hypothesis that the optimal CG position is 22 ± 1 cm rear of the front axle and the ideal mass is 867 ± 50 grams. This study highlights the significance of CG position in vehicle design.

B-double is one kind of vehicle combinations with three vehicle units structurally and widely applied in cargo transport, so its directional stability analysis is crucial but more complex than a car. Directional stability was related with vehicle operating conditions and structure parameters. A basic linear dynamic model with four degrees of freedom for B-double was developed with a special method for simplification in a calculation process. The method was verified from the simulation results with MATLAB. The step input is tractor’s steering wheel angle, so the steady-state yaw rate gains with understeer gradient for three vehicle units were solved. Steady-state relative gain was also derived and has an impact on directional stability, which could be influenced by the structure parameters, such as body mass (cargo weight), tire cornering stiffness of rear axle, location of mass center, wheelbase, and location of articulated point. The results indicate that increasing body mass and tire cornering stiffness of rear axle appropriately, moving backward mass center, and moving forward articulated points could increase the understeer gradient, which results in the improvement of directional stability. It is theoretical basis for the stability tests and structure design of B-double.

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Rollover index is an essential metric to detect vehicle rollover propensity, which is used to warn a driver or trigger an active rollover prevention system to perform corrective action. To give continuous and completed rollover information, this paper presents a novel rollover index based on a mass-center-position (MCP) metric. The MCP is first determined by estimating the positions of the center of mass of the vehicle, which consists of one sprung mass and two unsprung masses with two switchable roll motion models, before and after tire liftoff. The roll motion information can then be provided through the MCP continuously without saturation for both tripped and untripped rollovers. Moreover, to describe the completed rollover status, different criteria are derived from D'Alembert's principle and the moment balance conditions based on the MCP. In addition to tire liftoff, three new rollover statuses, “rollover threshold,” “rollover,” and vehicle jumping “in the air,” can be all identified by the proposed criteria. Furthermore, the sensitivity analysis is conducted on some key parameters, such as the sprung/unsprung masses and the height of the center of gravity, to validate the robustness of the proposed detection method. Compared with an existing representative rollover index, lateral load transfer ratio, the fishhook maneuver simulation results in CarSim for an electric vehicle show that the MCP metric can successfully predict vehicle impending rollover status continuously and completely for both untripped and tripped rollovers.

Due to the electrification of modern vehicles the role of the electric drive is growing as the main mover. In conditions of increasing requirements for the safety controllability and energy efficiency of a vehicle on electric traction,it is actual to take into account the dynamic properties of a vehicle in various driving modes when developing an automatic control system. In the work it is investigated the influence of the mass center position on the redistribution of forces during acceleration on a straight-line section. Taking into consideration the position of the mass center in the control system allows redistributing the desired moment to the wheels with better adhesion to the surface, which increases the safety and controllability of the vehicle, as well as minimizes energy costs on wheels with the worst adhesion. The aim of the work is to investigate the influence of the mass center position on the dynamics of a vehicle with full, rear and front wheel drive using computer simulation. The mathematical description includes analytical expressions for the redistribution of the support reactions for each of the wheels, which makes it possible, on their basis, to carry out computer simulation of the electric vehicle acceleration on a straight-line section. For the indicated types of vehicle drives, a computer model has been developed that includes, in the automatic control system for torque redistribution, the coordinates of the mass center position, which are converted on the basis of analytical expressions into the physical parameters of the system.Computer simulation of acceleration from zero to one hundred km/h with full pressing of the accelerator pedal for nine different positions of the mass center and three types of drive was carried out. Data were obtained on the change in accelerations, support reactions and torque of wheels during acceleration at various mass center positions. Based on the results obtained, the most preferable coordinates of the mass center for each type of drive from the point of view of the acceleration dynamics on a straight section were determined. The developed computer model can be used to study the dynamics of an electric vehicle when cornering, as well as to study energy indicators in all dynamic driving modes.

Both Transient Optimal and Quasi-Steady-State vehicle modelling are frequently used for minimum time manoeuvre and minimum lap-time simulations for motorsport/vehicle development purposes. Quasi-Steady-State methods have been shown to perform such tasks with low computational cost but produce results with measurable deviation from equivalent Transient Optimal studies. It is hypothesised that bounding point-mass models with jerk limits will improve alignment with Transient Optimal simulations through the consideration of transient behaviours such as control application rates and inertia. This work presents a new method for the determination of a vehicle's maximum jerk capacity from a seven degree-of-freedom vehicle model. A range of results from this proposed technique are presented and show the sensitivity of jerk capacity to changes in control application rate limits and yaw inertia, as well as current vehicle velocity, acceleration, and jerk. It is proposed that further exploration of the validity of the jerk limit calculation method take place through characterisation of said limits and application to a point-mass model, after which comparison to Transient Optimal and Quasi-Steady-State equivalents can occur.

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Attention to the theory of rolling stock oscillations is due primarily to the fact that oscillatory processes, which inevitably arise as a result of driving along a usually uneven road, degrade almost all the basic properties of rolling stock. The article considers oscillations of a four-axle vehicle with a double spring suspension. The study of oscillations with a finite number of degrees of freedom is simplified if we introduce the principal coordinates of this system. To simplify the finding of the principal coordinates, free and forced oscillations of the sprung parts of the vehicle are investigated. It is assumed that the body of the vehicle has two degrees of freedom: lateral motion and wabbling; bouncing and pitching of trolleys will be neglected. The total number of degrees of freedom of the model is two. Having set-up the kinetic and potential energy and using the Lagrange equations, a system of differential equations was obtained. Consideration of forced oscillations of a system with two degrees of freedom is greatly simplified when moving to the principal coordinates. The problem of the vertical dynamics of the rolling stock is simplified in the transition to the principal coordinates. The resulting differential equations of free and forced oscillations of the system in the principal coordinates are two independent second-order linear differential equations, which greatly simplifies their solution.

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Autonomous vehicle control is generally divided in two main areas; trajectory planning and tracking. Currently, the trajectory planning is mostly done by particle or kinematic model-based optimization controllers. The output of these planners, since they do not consider CG height and its effects, is not unique for different vehicle types, especially for high CG vehicles. As a result, the tracking controller may have to work hard to avoid vehicle handling and comfort constraints while trying to realize these sub-optimal trajectories. This paper tries to address this problem by considering a planner with simplified double track model with estimation of lateral and roll based load transfer using steady state equations and a simplified tire model to reduce solver workload. The developed planner is compared with the widely used particle and kinematic model planners in collision avoidance scenarios in both high and low acceleration conditions and with different vehicle heights.

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This paper proposes a nonlinear observer design for estimating the lateral load transfer ratio (LTR), a type of rollover index for vehicle safety, with a reduced reliance on measurable signals, applicable to both untripped and tripped rollovers. The dynamics of the four-degree-of-freedom vehicle are modeled to include tripped rollover, treating tire forces as unknown inputs. To address output nonlinearity, the observer employs a generalized inverse, offering an innovative solution. Benefiting from the unique structure of the state-space equations of the vehicle model, only three measurable signals are required for state and unknown input estimation. An algorithm for the observer is presented, ensuring the asymptotic stability of error through parametric solutions for matrix equations and the Lyapunov stability theorem. Validation conducted via Car-Sim simulations demonstrates the effectiveness of the proposed nonlinear observer in accurately estimating the vehicle states and tire forces. This accuracy positions it as a valuable tool for rollover prediction in vehicle safety applications.

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During the operation of outdoor heavy-duty Automated Guided Vehicle (AGV), the stability and safety of AGV are easily reduced due to load transfer. In order to solve this problem, a trajectory tracking control strategy considering load transfer is proposed to realize the trajectory tracking of AGV and the adaptive distribution of driving torque. The three-degree-of-freedom (3-DOF) kinematics model and pose error model of heavy-duty AGV vehicles are established. The lateral load transfer and longitudinal load transfer rules are analyzed. The vehicle trajectory tracking control strategy is composed of an improved model predictive controller (IMPC) and drive motor torque adaptive distribution controller considering load transfer. By optimizing the lateral acceleration of the vehicle body, the IMPC controller improves the problem of large driving force difference between the left and right sides of the wheel caused by the lateral transfer of the load and the problem of large wheel adhesion rate caused by the longitudinal transfer of the load is improved by the speed controller and the torque proportional distribution controller. The joint simulation platform of MATLAB/Simulink and CarSim is built to simulate and analyze the trajectory tracking of heavy-duty AGV under different pavement adhesion coefficients. The simulation results have shown that compared with the control strategy without considering load transfer, on the two types of pavements with different adhesion coefficients, the maximum lateral acceleration is reduced by 19.7%, and the maximum tire adhesion rate is reduced by 11.5%.

At present, research on the loading simulation test bench for overloaded vehicles is mostly limited to the realization of loading simulation under the condition of straight line driving of the vehicle. When simulating the whole vehicle load under straight line driving condition, only accurate road load and inertial load can be obtained to achieve accurate load simulation, and the inertial load required to be simulated by each side device is half of the whole vehicle inertial load. However, straight line driving is only the most ideal driving. The real driving conditions (including ground conditions) of the vehicle are usually composed of steering, longitudinal slope, lateral slope and different ground adhesion coefficients. Therefore, the loads borne by the two independent load simulation systems located on both sides of the vehicle may be different during the load simulation process. In addition, the frequency characteristics of the loads to be simulated by the same side load simulation device at different time sequences may also be different. This paper studies the dynamics modeling, load distribution and error compensation in the whole vehicle load simulation process in order to achieve accurate crawler vehicle load simulation from three aspects.

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With the rapid development of the automobile industry, vehicle durability has become one of the important indicators for measuring automobile performance. As a key factor affecting vehicle durability, road surface grade has a significant impact on the load borne by the vehicle during actual driving. This study aims to explore a method for compiling a vehicle durability load spectrum based on road surface grade, in order to provide a theoretical basis and technical support for automobile durability design and testing. First, this paper reviews the classification standards of road surface grades and the characterization methods of road surface roughness, and analyzes the influence of different road surface grades on vehicle load. Secondly, through field measurement and statistical analysis, a load model under different road surface grades is established, and a time domain load signal that conforms to the actual road surface characteristics is generated using the filtered white noise method. Furthermore, combined with vehicle dynamics and load transfer mechanism, the response characteristics of the vehicle under different road surface grades are studied, and the vehicle durability load spectrum is compiled. The method of this study is characterized by comprehensively considering the statistical characteristics of road surface roughness and the dynamic response of the vehicle, ensuring the practicality and accuracy of the load spectrum. The effectiveness of the compiled load spectrum is verified by simulation analysis of the load spectrum under typical road surface grades. The research results show that the load spectrum can provide load input close to actual road conditions for vehicle durability tests, which is of great significance for improving the level of automobile durability design.

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Driverless racing car requires accurate trajectory tracking for fastest driving. However, the extreme driving conditions make it difficult to achieve the accurate tracking performance while meeting the real-time control requirement. Therefore, this paper proposed a trajectory tracking controller based on nonlinear model predictive control (MPC) considering longitudinal load transfer. First, the vehicle dynamic model, including the load transfer between front and rear axles was built. Second, the optimal tracking problem is formulated to minimize the lateral and speed tracking errors while ensuring vehicle stability. In addition, the speed terminal constraint is added to prevent the runaway situation caused by small visual range. Finally, the driverless racing car is simulated in a track to follow a per-defined trajectory. By comparing to a QP-MPC controller, the proposed NMPC method improves the lap time by 1% while ensuring vehicle stability.

Collision avoidance (CA) systems have become a requirement in vehicles due to their ability to prevent collisions. Despite the implementation of these systems on the road, accidents still happen due to the lack of adaptability of CA systems corresponding to road environment nonlinearities and external disturbances. Hence, this research focuses on the effect of external disturbances, such as additional load distribution on the vehicle while avoiding obstacles. The deployment of the CA scenario, considering the presence of disturbance, was simulated in MATLAB Simulink, with the reference trajectory for the system obtained from a skilled driver in real-time experiments at different speeds. The objective of this study is to observe and analyse the effect of additional load disturbances on vehicle stability, especially when the driver countersteers to avoid an obstacle. An increase in the additional load percentage at each side of the vehicle produces excessive lateral force opposite to the direction of the vehicle. This scenario creates a significant load transfer phenomenon and directly causes the vehicle to oversteer and understeer while avoiding obstacles. It has been observed that human cognition plays a huge role in defining a reference trajectory at different speeds while avoiding an obstacle. The pattern of the reference trajectory also affects the magnitude of the load transfer phenomena, especially when the driver manoeuvres the vehicle aggressively.

When a tyre blows out, the vehicle’s trajectory deviates from the intended path, resulting in an accident and death. Because of its larger contact patch, applying more steering force to the flat tyre will cause the tyre to separate from the wheel. Also, improper braking effort causes rollover related issues due to the sudden vehicle’s centre of gravity (C.G.) displacement and weight transfer towards the blow-out tyre. Therefore, this research attempts to return the C.G. to its initial position through the suspension control. The force supplied between the sprung and unsprung masses of the flat tyre through suspension actuator is estimated and controlled by the model predictive control (MPC) scheme with respect to its input signals. For the non-linear simulation, the four-wheel passenger car vehicle dynamic model and the combined empirical model for the tyre inflation pressure effect are used. And the equivalent plant model is identified through a simple system identification method for the MPC design. The passive and active based suspension of standalone, roll-resistant interconnected and pitch-resistant interconnected systems, including the proposed comfort-lateral trajectory controlled standalone active suspension, were examined. To assess the effectiveness of each suspension and its control strategy in a tyre blow-out scenario, a vehicle was analysed with various longitudinal velocities along with and without steer input.

Accurate and reliable localization is of great importance for autonomous vehicles (AV). Mainstream localization approaches in autonomous vehicles (AV) are limited by the reliability of onboard sensors, which could be vulnerable to sensor failure, such as signal outages of the camera and signal spoofing of the global navigation satellite systems (GNSS). Different from these active or passive sensors, the vehicle dynamic model (VDM), which is the application of physical laws to a vehicle in motion, is environmentally independent and is capable of providing vehicle motion estimation continuously. However, the performance of the VDM-based motion estimation is dominated by the accuracy of the system dynamics model. To tackle this issue, this study proposes a sensor-free localization method VDM-SI by integrating system identification into the design of vehicle dynamic models (VDM). A system identification process based on low-order process models is proposed to identify the system dynamics of the AV, where the identified system responses are taken as the control input of VDM to estimate the vehicular positioning. The localization experiments in two scenarios show that the mean absolute translation error of VDM-SI can be reduced by 70% compared to conventional VDM methods. In addition, VDM-SI is experimentally proven to improve the localization performance of sensor fusion-based localization systems with high noise levels. Furthermore, in the application of re-localization after sensors fail and recover, VDM-SI shows strength in enhancing the security of AVs in extreme conditions.

Localization plays a vital role in various autonomous systems, providing essential information for perception and planning tasks. However, mainstream localization methods are based on the sensors approach, which is vulnerable in some extreme conditions where sensors probably fail in a short period, such as the camera-based visual positioning. This study proposes a sensor-free localization method by integrating vehicle dynamic models and an online system identification module. First, a system identification process is conducted online to identify the system dynamics of the powertrain system and the steering system of the autonomous vehicle. Then, the identified system responses are taken as the control input of the vehicle dynamic model to produce the positioning results. The simulated experiments show that the proposed method achieves better positioning performance than the conventional vehicle dynamic models. In addition, the extendibility of the proposed method is explored by fusing it with extra sensors based on the extended Kalman filter (EKF). Furthermore, the navigation ability of the proposed method without sensors is also examined along a trajectory of 140 meters. The proposed method successfully accomplishes the navigation task without any collisions, demonstrating the effectiveness in enhancing the security of autonomous systems with navigation needs when sensors fail in extreme conditions.

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Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.

The paper proposes a parameter identification method for a vehicle model using real measurements of onboard sensors. The motivation of the paper is to improve the localization of the vehicle when the accuracy of the regular methods is poor, e.g. in the case of unavailable GNSS signals, no enough feature for vision, or low acceleration for IMU-based techniques. In these situations the wheel encoder based odometry may be an appropriate choice for pose estimation, however, this method suffers from parameter uncertainty and unmodelled effects. The utilized vehicle model operates with dynamic wheel radius. The proposed identification method combines the Kalman-filter and least square techniques in an iterative loop for estimating the parameters. The estimation process is verified by real test of a compact car. The results are compared with the nominal setting, in which there is no estimation.

Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness.

No abstract available

Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate system model, as modeling errors and disturbances can degrade performance, making uncertainty management crucial. Learning-based MPC addresses this challenge by adapting the predictive model to changing and unmodeled conditions. However, existing approaches often involve trade-offs: robust methods tend to be overly conservative, stochastic methods struggle with real-time feasibility, and deep learning lacks interpretability. Sparse regression techniques provide an alternative by identifying compact models that retain essential dynamics while eliminating unnecessary complexity. In this context, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is particularly appealing, as it derives governing equations directly from data, balancing accuracy and computational efficiency. This work investigates the use of SINDy for learning and adapting vehicle dynamics models within an MPC framework. The methodology consists of three key phases. First, in offline identification, SINDy estimates the parameters of a three-degree-of-freedom single-track model using simulation data, capturing tire nonlinearities to create a fully tunable vehicle model. This is then validated in a high-fidelity CarMaker simulation to assess its accuracy in complex scenarios. Finally, in the online phase, MPC starts with an incorrect predictive model, which SINDy continuously updates in real time, improving performance by reducing lap time and ensuring a smoother trajectory. Additionally, a constrained version of SINDy is implemented to avoid obtaining physically meaningless parameters while aiming for an accurate approximation of the effects of unmodeled states. Simulation results demonstrate that the proposed framework enables an adaptive and efficient representation of vehicle dynamics, with potential applications to other control strategies and dynamical systems.

In this work, we study estimation problems in nonlinear mechanical systems subject to non-stationary and unknown excitation, which are common and critical problems in design and health management of mechanical systems. A primary-auxiliary model scheduling procedure based on time-domain transmissibilities is proposed and performed under switching linear dynamics: In addition to constructing a primary transmissibility family from the pseudo-inputs to the output during the offline stage, an auxiliary transmissibility family is constructed by further decomposing the pseudo-input vector into two parts. The auxiliary family enables to determine the unknown working condition at which the system is currently running at, and then an appropriate transmissibility from the primary transmissibility family for estimating the unknown output can be selected during the online estimation stage. As a result, the proposed approach offers a generalizable and explainable solution to the signal estimation problems in nonlinear mechanical systems in the context of switching linear dynamics with unknown inputs. A real-world application to the estimation of the vertical wheel force in a full vehicle system are, respectively, conducted to demonstrate the effectiveness of the proposed method. During the vehicle design phase, the vertical wheel force is the most important one among Wheel Center Loads (WCLs), and it is often measured directly with expensive, intrusive, and hard-to-install measurement devices during full vehicle testing campaigns. Meanwhile, the estimation problem of the vertical wheel force has not been solved well and is still of great interest. The experimental results show good performances of the proposed method in the sense of estimation accuracy for estimating the vertical wheel force.